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  • 1.
    Jin, Di
    et al.
    Tech Univ Darmstadt, Germany.
    Yin, Feng
    Chinese Univ Hong Kong, Peoples R China.
    Fritsche, Carsten
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Zoubir, Abdelhak M.
    Tech Univ Darmstadt, Germany.
    Bayesian Cooperative Localization Using Received Signal Strength With Unknown Path Loss Exponent: Message Passing Approaches2020Ingår i: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 68, s. 1120-1135Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    We propose a Bayesian framework for the received-signal-strength-based cooperative localization problem with unknown path loss exponent. Our purpose is to infer the marginal posterior of each unknown parameter: the position or the path loss exponent. This probabilistic inference problem is solved using message passing algorithms that update messages and beliefs iteratively. For numerical tractability, we combine the variable discretization and Monte-Carlo-based numerical approximation schemes. To further improve computational efficiency, we develop an auxiliary importance sampler that updates the beliefs with the help of an auxiliary variable. An important ingredient of the proposed auxiliary importance sampler is the ability to sample from a normalized likelihood function. To this end, we develop a stochastic sampling strategy that mathematically interprets and corrects an existing heuristic strategy. The proposed message passing algorithms are analyzed systematically in terms of computational complexity, demonstrating the computational efficiency of the proposed auxiliary importance sampler. Various simulations are conducted to validate the overall good performance of the proposed algorithms.

  • 2.
    Radnosrati, Kamiar
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Fritsche, Carsten
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Gunnarsson, Fredrik
    Ericsson Res, RAN Automat and Positioning, S-58112 Linkoping, Sweden.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Hendeby, Gustaf
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Localization in 3GPP LTE Based on One RTT and One TDOA Observation2020Ingår i: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 69, nr 3, s. 3399-3411Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    We study the fundamental problem of fusing one round trip time (RTT) observation associated with a serving base station with one time-difference of arrival (TDOA) observation associated to the serving base station and a neighbor base station to localize a 2-D mobile station (MS). This situation can arise in 3GPP Long Term Evolution (LTE) when the number of reported cells of the mobile station is reduced to a minimum in order to minimize the signaling costs and to support a large number of devices. The studied problem corresponds geometrically to computing the intersection of a circle with a hyperbola, both with measurement uncertainty, which generally has two equally likely solutions. We derive an analytical representation of these two solutions that fits a filter bank framework that can keep track of different hypothesis until potential ambiguities have been resolved. Further, a performance bound for the filter bank is derived. The proposed filter bank is first evaluated in a simulated scenario, where the set of serving and neighbor base stations is changing in a challenging way. The filter bank is then evaluated on real data from a field test, where the result shows a precision better than 40 m 95% of the time.

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  • 3.
    Alickovic, Emina
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten. Oticon AS, Denmark.
    Lunner, Thomas
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Institutet för handikappvetenskap (IHV). Oticon AS, Denmark; Tech Univ Denmark, Denmark.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Ljung, Lennart
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    A Tutorial on Auditory Attention Identification Methods2019Ingår i: Frontiers in Neuroscience, ISSN 1662-4548, E-ISSN 1662-453X, Vol. 13, artikel-id 153Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Auditory attention identification methods attempt to identify the sound source of a listeners interest by analyzing measurements of electrophysiological data. We present a tutorial on the numerous techniques that have been developed in recent decades, and we present an overview of current trends in multivariate correlation-based and model-based learning frameworks. The focus is on the use of linear relations between electrophysiological and audio data. The way in which these relations are computed differs. For example, canonical correlation analysis (CCA) finds a linear subset of electrophysiological data that best correlates to audio data and a similar subset of audio data that best correlates to electrophysiological data. Model-based (encoding and decoding) approaches focus on either of these two sets. We investigate the similarities and differences between these linear model philosophies. We focus on (1) correlation-based approaches (CCA), (2) encoding/decoding models based on dense estimation, and (3) (adaptive) encoding/decoding models based on sparse estimation. The specific focus is on sparsity-driven adaptive encoding models and comparing the methodology in state-of-the-art models found in the auditory literature. Furthermore, we outline the main signal processing pipeline for how to identify the attended sound source in a cocktail party environment from the raw electrophysiological data with all the necessary steps, complemented with the necessary MATLAB code and the relevant references for each step. Our main aim is to compare the methodology of the available methods, and provide numerical illustrations to some of them to get a feeling for their potential. A thorough performance comparison is outside the scope of this tutorial.

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  • 4.
    Forsling, Robin
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten. Saab AB, Linkoping, Sweden.
    Sjanic, Zoran
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten. Saab AB, Linkoping, Sweden.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Hendeby, Gustaf
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Consistent Distributed Track Fusion Under Communication Constraints2019Ingår i: Proceedings of the 22nd International Conference on Information Fusion (FUSION), IEEE, 2019Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper addresses the problem of retrieving consistentestimates in a distributed network where the communication between the nodes is constrained such that only the diagonal elements of the covariance matrix are allowed to be exchanged. Several methods are developed for preserving and/or recovering consistency under the constraints imposed by the communication protocol. The proposed methods are used in conjunction with the covariance intersection method and the estimation performance is evaluated based on information usage and consistency. The results show that among the proposed methods, consistency can be preserved equally well at the transmitting node as at the receiving node.

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  • 5.
    Skoglund, Martin
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Gustafsson, Fredrik
    Linköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Hendeby, Gustaf
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Tekniska fakulteten. Linköping University.
    On Iterative Unscented Kalman Filter using Optimization2019Ingår i: Proceedings of the 22nd International Conferenceon Information Fusion, 2019Konferensbidrag (Refereegranskat)
    Abstract [en]

    The unscented Kalman filter (UKF) is a very popular solution for estimation of the state in nonlinear systems. Similar to the extended Kalman filter (EKF) and contrary to the Kalman filter (KF) for linear systems, the UKF provides no guarantees that the filter updates will improve the filtered state estimate. In the past, the iterated EKF (IEKF) has been suggested as a way to online monitor the filter performance and try to improve it using optimization techniques. In this paper we do the same for the UKF, deriving six iterated UKF (IUKF) variations based on two cost functions and three optimization algorithms. The methods are evaluated and compared to IEKF versions and to two versions of the iterative posterior linearization filter (IPLF) in three benchmark simulation studies. The results show that IUKF algorithms can be used as a derivative free alternative to IEKF, and provide insights about the different design choices available in IUKF algorithms.

  • 6.
    Sundbom, Per
    et al.
    Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för kardiovaskulär medicin. Linköpings universitet, Medicinska fakulteten. Region Östergötland, Hjärt- och Medicincentrum, Kardiologiska kliniken US. Department of Medicine and Geriatrics, Höglandet Hospital, Eksjö.
    Roth, Michael
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Granfeldt, Hans
    Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för kardiovaskulär medicin. Linköpings universitet, Medicinska fakulteten. Region Östergötland, Hjärt- och Medicincentrum, Thorax-kärlkliniken i Östergötland.
    Karlsson, Daniel M.
    Linköpings universitet, Institutionen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Ahn, Henrik
    Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för kardiovaskulär medicin. Linköpings universitet, Medicinska fakulteten. Region Östergötland, Hjärt- och Medicincentrum, Thorax-kärlkliniken i Östergötland.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Dellgren, Göran
    Department of Cardiothoracic Surgery, Sahlgrenska University Hospital, Gothenburg; Transplant Institute, Sahlgrenska University Hospital, Gothenburg.
    Hübbert, Laila
    Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för kardiovaskulär medicin. Linköpings universitet, Medicinska fakulteten. Region Östergötland, Hjärt- och Medicincentrum, Kardiologiska kliniken i Norrköping. Transplant Institute, Sahlgrenska University Hospital, Gothenburg.
    Sound analysis of the magnetically levitated left ventricular assist device HeartMate 32019Ingår i: International Journal of Artificial Organs, ISSN 0391-3988, E-ISSN 1724-6040, Vol. 42, nr 12, s. 717-724Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    INTRODUCTION: The HeartMate 3 has shown lower rates of adverse events compared to previous devices due to the design and absence of mechanical bearings. For previous devices, sound analysis emerged as a way to assess pump function. The aims of this study were to determine if sound analysis can be applied to the HeartMate 3 in vivo and in vitro and to evaluate an electronic stethoscope.

    METHOD: Sound recordings were performed with microphones and clinical accessible electronic stethoscope. The recordings were studied in both the time and the frequency domains. Recordings from four patients were performed to determine if in vivo and in vitro recordings are comparable.

    RESULTS: The results show that it is possible to detect sound from HeartMate 3 and the sound spectrum is clear. Pump frequency and frequency of the pulsatile mode are easily determined. Frequency spectra from in vitro and in vivo recordings have the same pattern, and the major proportion (96.7%) of signal power is located at the pump speed frequency ±40 Hz. The recordings from the patients show low inter-individual differences except from location of peaks originating from pump speed and harmonics. Electronic stethoscopes could be used for sound recordings, but the dedicated equipment showed a clearer sound spectrum.

    DISCUSSION: The results show that acoustic analysis can also be performed with the HeartMate 3 and that in vivo and in vitro sound spectrum is similar. The frequency spectra are different from previous devices, and methods for assessing pump function or thrombosis need further evaluation.

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  • 7.
    Veibäck, Clas
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Hendeby, Gustaf
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Uncertain Timestamps in Linear State Estimation2019Ingår i: IEEE Transactions on Aerospace and Electronic Systems, ISSN 0018-9251, E-ISSN 1557-9603, Vol. 55, nr 3, s. 1334-1346Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    We consider a linear state estimation problem, where, in addition to the usual timestamped measurements, observations with uncertain timestamps are available. Such observations could, e.g., come from traces left by a target in a tracking scenario or from witnesses of an event and have the potential to improve the estimation accuracy significantly. We derive the posterior distribution and point estimators for a linear Gaussian smoothing formulation of this problem and illustrate with two numerical examples.

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    Uncertain Timestamps in Linear State Estimation
  • 8.
    Fritsche, Carsten
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Orguner, Umut
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Bobrovsky-Zakai Bound for Filtering, Prediction and Smoothing of Nonlinear Dynamic Systems2018Ingår i: 2018 21st International Conference on Information Fusion (FUSION), 2018, s. 1-8Konferensbidrag (Refereegranskat)
    Abstract [en]

    In this paper, recursive Bobrovsky-Zakai bounds for filtering, prediction and smoothing of nonlinear dynamic systems are presented. The similarities and differences to an existing Bobrovsky-Zakai bound in the literature for the filtering case are highlighted. The tightness of the derived bounds are illustrated on a simple example where a linear system with non-Gaussian measurement likelihood is considered. The proposed bounds are also compared with the performance of some well known filters/predictors/smoothers and other Bayesian bounds.

  • 9.
    Skog, Isaac
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Hendeby, Gustaf
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Magnetic Odometry - A Model-Based Approach Using A Sensor Array2018Ingår i: 2018 21st International Conference on Information Fusion (FUSION), 2018, s. 794-798Konferensbidrag (Refereegranskat)
    Abstract [en]

    A model-based method to perform odometry using an array of magnetometers that sense variations in a local magnetic field is presented. The method requires no prior knowledge of the magnetic field, nor does it compile any map of it. Assuming that the local variations in the  magnetic field can be described by a curl and divergence free polynomial model, a maximum likelihood estimator is derived. To gain insight into the array design criteria and the achievable estimation performance, the identifiability conditions of the estimation problem are analyzed and the Cramér-Rao bound for the one-dimensional case is derived. The analysis shows that with a second-order model it is sufficient to have six magnetometer triads in a plane to obtain local identifiability. Further, the Cramér-Rao bound shows that the estimation error is inversely proportional to the ratio between the rate of change of the magnetic field and the noise variance, as well as the length scale of the array. The performance of the proposed estimator is evaluated using real-world data. The results show that, when there are sufficient variations in the magnetic field, the estimation error is of the order of a few percent of the displacement. The method also outperforms current state-of-theart method for magnetic odometry

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  • 10.
    Fritsche, Carsten
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Orguner, Umut
    Middle East Technical University, Turkey.
    Özkan, Emre
    Middle East Technical University, Turkey.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Marginal Bayesian Bhattacharyya Bounds for discrete-time filtering2018Ingår i: Proc. of 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, Canada, 2018, IEEE, 2018, s. 4289-4293Konferensbidrag (Refereegranskat)
    Abstract [en]

    In this paper, marginal versions of the Bayesian Bhattacharyya lower bound (BBLB), which is a tighter alternative to the classical Bayesian Cramer-Rao bound, for discrete-time filtering are proposed. Expressions for the second and third-order marginal BBLBs are obtained and it is shown how these can be approximately calculated using particle filtering. A simulation example shows that the proposed bounds predict the achievable performance of the filtering algorithms better.

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  • 11.
    Zhao, Yuxin
    et al.
    Research, Ericsson AB, 39174 Stockholm, Sweden.
    Fritsche, Carsten
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Yin, Feng
    SSE, Chinese University of Hong Kong Shenzhen, Shenzhen, China.
    Gunnarsson, Fredrik
    Ericsson Research, Linköping, Sweden.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Sequential Monte Carlo Methods and Theoretical Bounds for Proximity Report Based Indoor Positioning2018Ingår i: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 67, nr 6, s. 5372-5386Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The commercial interest in proximity services is increasing. Application examples include location-based information and advertisements, logistics, social networking, file sharing, etc. In this paper, we consider positioning of devices based on a time series of proximity reports from a mobile device to a network node. This corresponds to nonlinear measurements with respect to the device position in relation to the network nodes. Motion model will be needed together with the measurements to determine the position of the device. Therefore, sequential Monte Carlo methods, namely particle filtering and smoothing, are applicable for positioning. Positioning performance is evaluated in a typical office area with Bluetooth-low-energy beacons deployed for proximity detection and report, and is further compared to parametric Cramér-Rao lower bounds. Finally, the position accuracy is also evaluated with real experimental data.

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  • 12.
    Nurminen, Henri
    et al.
    Tampere Univ Technol, Finland; HERE Technol, Finland.
    Ardeshiri, Tohid
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten. Univ Cambridge, England.
    Piche, Robert
    Tampere Univ Technol, Finland.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Skew-t Filter and Smoother With Improved Covariance Matrix Approximation2018Ingår i: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 66, nr 21, s. 5618-5633Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Filtering and smoothing algorithms for linear discrete-time state-space models with skew-t-distributed measurement noise are proposed. The algorithms use a variational Bayes based posterior approximation with coupled location and skewness variables to reduce the error caused by the variational approximation. Although the variational update is done suboptimally using an expectation propagation algorithm, our simulations show that the proposed method gives a more accurate approximation of the posterior covariance matrix than an earlier proposed variational algorithm. Consequently, the novel filter and smoother outperform the earlier proposed robust filter and smoother and other existing low-complexity alternatives in accuracy and speed. We present both simulations and tests based on real-world navigation data, in particular the global positioning system data in an urban area, to demonstrate the performance of the novel methods. Moreover, the extension of the proposed algorithms to cover the case where the distribution of the measurement noise is multivariate skew-t is outlined. Finally, this paper presents a study of theoretical performance bounds for the proposed algorithms.

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  • 13.
    Sundbom, Per
    et al.
    Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för kardiovaskulär medicin. Linköpings universitet, Medicinska fakulteten. Höglandssjukhuset, Sweden.
    Roth, Michael
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Granfeldt, Hans
    Linköpings universitet, Medicinska fakulteten. Region Östergötland, Hjärt- och Medicincentrum, Thorax-kärlkliniken i Östergötland. Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för kardiovaskulär medicin.
    Karlsson, Daniel
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Ahn, Henrik Casimir
    Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för kardiovaskulär medicin. Linköpings universitet, Medicinska fakulteten. Region Östergötland, Hjärt- och Medicincentrum, Thorax-kärlkliniken i Östergötland.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Hübbert, Laila
    Linköpings universitet, Institutionen för medicin och hälsa, Avdelningen för kardiovaskulär medicin. Linköpings universitet, Medicinska fakulteten. Region Östergötland, Hjärt- och Medicincentrum, Kardiologiska kliniken US. Karolinska Univ Hosp, Sweden; Karolinska Inst, Sweden.
    Sound analysis of a left ventricular assist device: A technical evaluation of iOS devices2018Ingår i: International Journal of Artificial Organs, ISSN 0391-3988, E-ISSN 1724-6040, Vol. 41, nr 5, s. 254-260Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Introduction: The use of left ventricular assist device (LVAD) has grown rapidly. Adverse events do continue to occur. In recent years, analysis of LVAD sound recordings emerged as a means to monitor pump function and detect pump thrombosis. The aim of this study was to characterize the sounds from HeartMate II and to evaluate the use of handheld iOS devices for sound recordings. Method: Signal analysis of LVAD sound recordings, with dedicated recording equipment and iOS devices, was performed. Two LVADs running in mock loop circuits were compared to an implanted LVAD. Spectral analysis and parametric signal models were explored to quantify the sound and potentially detect changes in it. Results: The sound recordings of two LVADs in individual mock loop circuits and a third one implanted in a patient appeared to be similar. Qualitatively, sound characteristics were preserved following changes in pump speed. Recordings using dedicated equipment showed that HeartMate II sound comprises low-frequency components corresponding to pump impeller rotation, as well as high-frequency components due to a pulse width modulation of the electric power to the pump. These different signal components interact and result in a complicated frequency spectrum. The iPhone and iPod recordings could not reproduce the sounds as well as the dedicated equipment. In particular, lower frequencies were affected by outside disturbances. Discussion: This article outlines a systematic approach to LVAD sound analysis using signal processing methods to quantify and potentially detect changes, and describes some of the challenges, for example, with the use of inexpensive recording devices.

  • 14.
    Fritsche, Carsten
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Bayesian Bhattacharyya bound for discrete-time filtering revisited2017Ingår i: Proc. of 2017 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2017, s. 719-723Konferensbidrag (Refereegranskat)
    Abstract [en]

    In this paper, the derivation of the Bayesian Bhattacharyya bound for discrete-time filtering as proposed ina paper by Reece and Nicholson is revisited. It turns out that the results presented in the aforementioned contribution are incorrect, as some expectations appearing in the information matrix recursions are missing. This paper gives a generalized derivation of the N-th order Bayesian Bhattacharyya bound and presents corrected expressions for the case N = 2. A nonlinear toy example is used to illustrate the results

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    Bayesian Bhattacharyya bound for discrete-time filtering revisited
  • 15.
    Roth, Michael
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Computation and visualization of posterior densities in scalar nonlinear and non-Gaussian Bayesian filtering and smoothing problems2017Ingår i: 2017 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), IEEE , 2017, s. 4686-4690Konferensbidrag (Refereegranskat)
    Abstract [en]

    One-dimensional Bayesian filtering and smoothing problems can be solved numerically using a number of algorithms, even in nonlinear and non-Gaussian cases. In this educational paper we advocate for the benefits of visualizing the obtained posterior densities as complement to, e.g., estimation error analysis. In addition to a review of Bayesian filtering and smoothing and the respective point mass and particle solutions, we devise a novel algorithm for filtering when the likelihood cannot be evaluated. Several instructive examples are discussed and easily adjustable matlab code is provided as complement to this paper.

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  • 16.
    Sjanic, Zoran
    et al.
    Linköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för systemteknik, Reglerteknik.
    Skoglund, Martin A.
    Linköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för systemteknik, Reglerteknik.
    Gustafsson, Fredrik
    Linköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för systemteknik, Reglerteknik.
    EM-SLAM with Inertial/Visual Applications2017Ingår i: IEEE Transactions on Aerospace and Electronic Systems, ISSN 0018-9251, E-ISSN 1557-9603, Vol. 53, nr 1, s. 273-285Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The general Simultaneous Localisation and Mapping (SLAM) problem aims at estimating the state of a moving platform simultaneously with building a map of the local environment. There are essentially three classes of algorithms. EKF- SLAM and FastSLAM solve the problem on-line, while Nonlinear Least Squares (NLS) is a batch method. All of them scales badly with either the state dimension, the map dimension or the batch length. We investigate the EM algorithm for solving a generalized version of the NLS problem. This EM-SLAM algorithm solves two simpler problems iteratively, hence it scales much better with dimensions. The iterations switch between state estimation, where we propose an Extended Rauch-Tung-Striebel smoother, and map estimation, where a quasi-Newton method is suggested. The proposed method is evaluated in real experiments and also in simulations on a platform with a monocular camera attached to an inertial measurement unit. It is demonstrated to produce lower RMSE than with a standard Levenberg-Marquardt solver of NLS problem, at a computational cost that increases considerably slower. 

  • 17.
    Gustafsson, Fredrik
    et al.
    Linköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Hendeby, Gustaf
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Tekniska fakulteten.
    Exploring New Localization Applications Using a Smartphone2017Ingår i: Sensing and Control for Autonomous Vehicles: Applications to Land, Water and Air Vehicles / [ed] Thor I. Fossen, Kristin Y. Pettersen, Henk Nijmeijer, Springer, 2017, s. 161-179Kapitel i bok, del av antologi (Refereegranskat)
    Abstract [en]

    Localization is an enabling technology in many applications and services today and in the future. Satellite navigation often works fine for navigation, infotainment and location based services, and it is today the dominating solution in commercial products. A nice exception is the localization in Google Maps, where radio signal strength from WiFi and cellular networks are used as complementary information to increase accuracy and integrity. With the on-going trend with more autonomous functions being introduced in our vehicles and with all our connected devices, most of them operated in indoor enviroments where satellite signals are not available,there is an acute need for new solutions.

    At the same time, our smartphones are getting more sophisticated in their sensor configuration. Therefore, in this chapter we present a freely available Sensor Fusion app developed in-house, how it works, how it has been used, and how it can be used based on a variety of applications in our research and student projects.

  • 18.
    Braga, Andre R.
    et al.
    University of Federal Ceara, Brazil.
    Fritsche, Carsten
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Bruno, Marcelo G. S.
    Aeronaut Institute Technology, Brazil.
    Gradient-Based Recursive Maximum Likelihood Identification of Jump Markov Non-Linear Systems2017Ingår i: 2017 20TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION), IEEE , 2017, s. 228-234Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper deals with state inference and parameter identification in Jump Markov Non-Linear System. The state inference problem is solved efficiently using a recently proposed Rao-Blackwellized Particle Filter, where the discrete state is integrated out analytically. Within the RBPF framework, Recursive Maximum Likelihood parameter identification is performed using gradient ascent algorithms. The proposed learning method has the advantage over (online) Expectation Maximization methods, that it can be easily applied to cases where the probability density functions defining the Jump Markov Non-Linear System are not members of the exponential family. Two benchmark problems illustrate the parameter identification performance.

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  • 19.
    Kasebzadeh, Parinaz
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Hendeby, Gustaf
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Fritsche, Carsten
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Gunnarsson, Fredrik
    Ericsson Research, Linkoping, Sweden.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    IMU Dataset For Motion and Device Mode Classification2017Ingår i: 2017 INTERNATIONAL CONFERENCE ON INDOOR POSITIONING AND INDOOR NAVIGATION (IPIN), IEEE , 2017Konferensbidrag (Refereegranskat)
    Abstract [en]

    Classification of motion mode (walking, running, standing still) and device mode (hand-held, in pocket, in back-pack) is an enabler in personal navigation systems for the purpose of saving energy and design parameter settings and also for its own sake. Our main contribution is to publish one of the most extensive datasets for this problem, including inertial data from eight users, each one performing three pre-defined trajectories carrying four smartphones and seventeen inertial measurement units on the body. All kind of metadata is available such as the ground truth of all modes and position. A second contribution is the first study on a joint classifier of motion and device mode, respectively, where preliminary but promising results are presented.

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  • 20.
    Fritsche, Carsten
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Karlsson, G Rickard
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten. NIRA Dynam AB, Linköping, Sweden.
    Noren, Olle
    NIRA Dynam AB, Linkoping, Sweden.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten. NIRA Dynam AB, Linkoping, Sweden.
    Map-Aided Multi-Level Indoor Vehicle Positioning2017Ingår i: 2017 INTERNATIONAL CONFERENCE ON INDOOR POSITIONING AND INDOOR NAVIGATION (IPIN), IEEE , 2017Konferensbidrag (Refereegranskat)
    Abstract [en]

    In this paper, an indoor vehicle multi-level positioning algorithm is proposed that makes use of an indoor map, as well as dead-reckoning sensor information that is available in every car. A particle filter framework is used for online optimal Bayesian vehicle positioning with indoor-outdoor transitions. The method is validated experimentally in two indoor multi-level car parks. The achieved results indicate that accurate indoor positioning is possible already today without relying on expensive technology such as e.g. laser scanners or additional hardware.

  • 21.
    Lindfors, Martin
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Hendeby, Gustaf
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Gustafsson, Fredrik
    Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Institutionen för systemteknik, Reglerteknik.
    Karlsson, Rickard
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    On Frequency Tracking in Harmonic Acoustic Signals2017Ingår i: Proceedings of the 2017 20th International Conference on Information Fusion (FUSION)., IEEE, 2017Konferensbidrag (Refereegranskat)
    Abstract [en]

    Acoustic frequency tracking of a harmonic signalwith continuously varying frequency is considered. The Rao-Blackwellized point mass filter (RBPMF), previously proposed bythe authors for mechanical vibration tracking, is applied to the problem. The RBPMF is compared with two periodogram-based methods, and the similarities and differences between them are explained. Both experimental and simulation results in a Doppler frequency tracking scenario are presented, and the results show that the RBPMF can have significantly less estimation error than the competing methods.

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  • 22.
    Radnosrati, Kamiar
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Hendeby, Gustaf
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Fritsche, Carsten
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Gunnarsson, Fredrik
    Ericsson Research.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Performance of OTDOA Positioning in Narrowband IoT Systems2017Ingår i: 28th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC): Proceedings, IEEE, 2017Konferensbidrag (Refereegranskat)
    Abstract [en]

    Narrowband Internet of Things (NB-IoT) is an emerging cellular technology designed to target low-cost devices, high coverage, long device battery life (more than ten years), and massive capacity. We investigate opportunities for device tracking in NB-IoT systems using Observed Time Difference of Arrival (OTDOA) measurements. Reference Signal Time Difference (RSTD) reports are simulated to be sent to the mobile location center periodically or on an ondemand basis. We investigate the possibility of optimizing the number of reports per minute budget on horizontal positioning accuracy using an on-demand reporting method based on the Signal to Noise Ratio (SNR) of the measured cells received by the User Equipment (UE). Wireless channels are modeled considering multipath fading propagation conditions. Extended Pedestrian A (EPA) and Extended Typical Urban (ETU) delay profiles corresponding to low and high delay spread environments, respectively, are simulated for this purpose. To increase the robustness of the filtering method, measurement noise outliers are detected using confidence bounds estimated from filter innovations.

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  • 23.
    Hendeby, Gustaf
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Wahlström, Niklas
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Gunnarsson, Svante
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Platform for Teaching Sensor Fusion Using a Smartphone2017Ingår i: International journal of engineering education, ISSN 0949-149X, Vol. 33, nr 2B, s. 781-789Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A platform for sensor fusion consisting of a standard smartphone equipped with the specially developed Sensor Fusion appis presented. The platform enables real-time streaming of data over WiFi to a computer where signal processingalgorithms, e.g., the Kalman filter, can be developed and executed in a Matlab framework. The platform is an excellenttool for educational purposes and enables learning activities where methods based on advanced theory can be implementedand evaluated at low cost. The article describes the app and a laboratory exercise developed around these new technologicalpossibilities. The laboratory session is part of a course in sensor fusion, a signal processing continuation course focused onmultiple sensor signal applications, where the goal is to give the students hands on experience of the subject. This is done byestimating the orientation of the smartphone, which can be easily visualized and also compared to the built-in filters in thesmartphone. The filter can accept any combination of sensor data from accelerometers, gyroscopes, and magnetometers toexemplify their importance. This way different tunings and tricks of important methods are easily demonstrated andevaluated on-line. The presented framework facilitates this in a way previously impossible.

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  • 24.
    Braga, André R.
    et al.
    Federal University of Ceara, Quixada, Brazil.
    Fritsche, Carsten
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Bruno, Marcelo G. S.
    Aeronautics Institute of Technology, Sao Jose dos Campos, Brazil.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Rapid System Identification for Jump Markov Non-Linear Systems2017Ingår i: Proc. 2017 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), IEEE, 2017, s. 714-718Konferensbidrag (Refereegranskat)
    Abstract [en]

    This work evaluates a previously introduced algorithm called Particle-Based Rapid Incremental Smoother within the framework of state inference and parameter identification in Jump Markov Non-Linear System. It is applied to the recursive form of two well-known Maximum Likelihood based algorithms who face the common challenge of online computation of smoothed additive functionals in order to accomplish the task of model parameter estimation. This work extends our previous contributions on identification of Markovian switching systems with the goal to reduce the computational complexity. A benchmark problem is used to illustrate the results.

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  • 25.
    Yin, Feng
    et al.
    Chinese University of Hong Kong, China.
    Zhao, Yuxin
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Gunnarsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten. Ericsson Research .
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Received-Signal-Strength Threshold Optimization Using Gaussian Processes2017Ingår i: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 65, nr 8, s. 2164-2177Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    There is a big trend nowadays to use event-triggered proximity report for indoor positioning. This paper presents a generic received-signal-strength (RSS) threshold optimization framework for generating informative proximity reports. The proposed framework contains five main building blocks, namely the deployment information, RSS model, positioning metric selection, optimization process and management. Among others, we focus on Gaussian process regression (GPR)-based RSS models and positioning metric computation. The optimal RSS threshold is found through minimizing the best achievable localization root-mean-square-error formulated with the aid of fundamental lower bound analysis. Computational complexity is compared for different RSS models and different fundamental lower bounds. The resulting optimal RSS threshold enables enhanced performance of new fashioned low-cost and low-complex proximity report-based positioning algorithms. The proposed framework is validated with real measurements collected in an office area where bluetooth-low-energy (BLE) beacons are deployed.

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    Received-Signal-Strength Threshold Optimization Using Gaussian Processes
  • 26.
    Rui, Rafael
    et al.
    University of Federal Rio Grande do Sul, Brazil.
    Ardeshiri, Tohid
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten. University of Cambridge, England.
    Nurminen, Henri
    Tampere University of Technology, Finland.
    Bazanella, Alexandre
    University of Federal Rio Grande do Sul, Brazil.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    State Estimation for a Class of Piecewise Affine State-Space Models2017Ingår i: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 24, nr 1, s. 61-65Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    n/a

  • 27.
    Roth, Michael
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Hendeby, Gustaf
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Fritsche, Carsten
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    The Ensemble Kalman filter: a signal processing perspective2017Ingår i: EURASIP Journal on Advances in Signal Processing, ISSN 1687-6172, E-ISSN 1687-6180, artikel-id 56Artikel, forskningsöversikt (Refereegranskat)
    Abstract [en]

    The ensemble Kalman filter (EnKF) is a Monte Carlo-based implementation of the Kalman filter (KF) for extremely high-dimensional, possibly nonlinear, and non-Gaussian state estimation problems. Its ability to handle state dimensions in the order of millions has made the EnKF a popular algorithm in different geoscientific disciplines. Despite a similarly vital need for scalable algorithms in signal processing, e.g., to make sense of the ever increasing amount of sensor data, the EnKF is hardly discussed in our field. This self-contained review is aimed at signal processing researchers and provides all the knowledge to get started with the EnKF. The algorithm is derived in a KF framework, without the often encountered geoscientific terminology. Algorithmic challenges and required extensions of the EnKF are provided, as well as relations to sigma point KF and particle filters. The relevant EnKF literature is summarized in an extensive survey and unique simulation examples, including popular benchmark problems, complement the theory with practical insights. The signal processing perspective highlights new directions of research and facilitates the exchange of potentially beneficial ideas, both for the EnKF and high-dimensional nonlinear and non-Gaussian filtering in general.

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  • 28.
    Karlsson, Rickard
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Gustafsson, Fredrik
    Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Institutionen för systemteknik, Reglerteknik.
    The Future of Automotive Localization Algorithms: Available, reliable, and scalable localization: Anywhere and anytime2017Ingår i: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 34, nr 2, s. 60-69Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Most navigation systems today rely on global navigation satellite systems (gnss), including in cars. With support from odometry and inertial sensors, this is a sufficiently accurate and robust solution, but there are future demands. Autonomous cars require higher accuracy and integrity. Using the car as a sensor probe for road conditions in cloud-based services also sets other kind of requirements. The concept of the Internet of Things requires stand-alone solutions without access to vehicle data. Our vision is a future with both invehicle localization algorithms and after-market products, where the position is computed with high accuracy in gnss-denied environments. We present a localization approach based on a prior that vehicles spend the most time on the road, with the odometer as the primary input. When wheel speeds are not available, we present an approach solely based on inertial sensors, which also can be used as a speedometer. The map information is included in a Bayesian setting using the particle filter (PF) rather than standard map matching. In extensive experiments, the performance without gnss is shown to have basically the same quality as utilizing a gnss sensor. Several topics are treated: virtual measurements, dead reckoning, inertial sensor information, indoor positioning, off-road driving, and multilevel positioning.

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  • 29.
    Bergström, Andreas
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten. Ericsson Research.
    Hendeby, Gustaf
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Gunnarsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten. Ericsson Research.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    TOA Estimation Improvements in Multipath Environments by Measurement Error Models2017Ingår i: Proceedings of the 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Institute of Electrical and Electronics Engineers (IEEE), 2017, s. 1-8Konferensbidrag (Refereegranskat)
    Abstract [en]

    Many positioning systems rely on accuratetime of arrival measurements. In this paper, we addressnot only the accuracy but also the relevance of Time ofArrival (TOA) measurement error modeling. We discusshow better knowledge of these errors can improve relativedistance estimation, and compare the impact of differentlydetailed measurement error information. These models arecompared in simulations based on models derived froman Ultra Wideband (UWB) measurement campaign. Theconclusion is that significant improvements can be madewithout providing detailed received signal information butwith a generic and relevant measurement error model.

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    TOA Estimation Improvements in Multipath Environments by Measurement Error Models
  • 30.
    Örn, Daniel
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Szilassy, Martin
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Dil, Bram
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    A Novel Multi-Step Algorithm for Low-Energy Positioning Using GPS2016Ingår i: Fusion 2016, 19th International Conference on Information Fusion: Proceedings, 2016, s. 1469-1476Konferensbidrag (Refereegranskat)
    Abstract [en]

    GPS is widely used for localization and tracking, however traditional GPS receivers consume too much energy for many applications. This paper implements and evaluates the performance of a low-energy GPS prototype. The main difference is that a traditional GPS needs to sample signals transmitted by satellites for 30 seconds to estimate its position. Our prototype reduces this time by three orders of magnitude and it can compute positions from only 2 milliseconds of data. We present a new algorithm that increases robustness by filtering on estimated residuals instead of using an altitude database. In addition, we show that our new algorithm works with both fixed and moving targets. The solution consists of (1) a portable device that samples the GPS signal and (2) a server that utilizes Doppler navigation and Coarse Time Navigation to estimate positions. We performed tests in a wide variety of environments and situations. These tests show that our prototype provides a median positioning error of roughly 40 meters even when the GPS receiver is moving at 80 kilometres per hour.

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  • 31.
    Alickovic, Emina
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Lunner, Thomas
    Linköpings universitet, Institutet för handikappvetenskap (IHV). Linköpings universitet, Institutionen för klinisk och experimentell medicin, Avdelningen för neuro- och inflammationsvetenskap. Linköpings universitet, Medicinska fakulteten. Eriksholm Research Centre, Oticon A/S, 20 Rortangvej, Snekkersten, Denmark.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    A System Identification Approach to Determining Listening Attention from EEG Signals2016Ingår i: 2016 24TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO), IEEE , 2016, s. 31-35Konferensbidrag (Refereegranskat)
    Abstract [en]

    We still have very little knowledge about how ourbrains decouple different sound sources, which is known assolving the cocktail party problem. Several approaches; includingERP, time-frequency analysis and, more recently, regression andstimulus reconstruction approaches; have been suggested forsolving this problem. In this work, we study the problem ofcorrelating of EEG signals to different sets of sound sources withthe goal of identifying the single source to which the listener isattending. Here, we propose a method for finding the number ofparameters needed in a regression model to avoid overlearning,which is necessary for determining the attended sound sourcewith high confidence in order to solve the cocktail party problem.

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  • 32.
    Dil, Bram
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Hendeby, Gustaf
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Hoenders, Bernhard
    University of Groningen, Centre for Theoretical Physics, Zernike Institute for Advanced Materials.
    Approximate Diagonalized Covariance Matrix for Signals with Correlated Noise2016Ingår i: Proceedings of the 19th International Conference of Information Fusion, Institute of Electrical and Electronics Engineers (IEEE), 2016, s. 521-527Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper proposes a diagonal covariance matrix approximation for Wide-Sense Stationary (WSS) signals with correlated Gaussian noise. Existing signal models that incorporate correlations often require regularization of the covariance matrix, so that the covariance matrix can be inverted. The disadvantage of this approach is that matrix inversion is computational intensive and regularization decreases precision. We use Bienayme's theorem to approximate the covariance matrix by a diagonal one, so that matrix inversion becomes trivial, even with nonuniform rather than only uniform sampling that was considered in earlier work. This approximation reduces the computational complexity of the estimator and estimation bound significantly. We numerically validate this approximation and compare our approach with the Maximum Likelihood Estimator (MLE) and Cramer-Rao Lower Bound (CRLB) for multivariate Gaussian distributions. Simulations show that our approach differs less than 0.1% from this MLE and CRLB when the observation time is large compared to the correlation time. Additionally, simulations show that in case of non-uniform sampling, we increase the performance in comparison to earlier work by an order of magnitude. We limit this study to correlated signals in the time domain, but the results are also applicable in the space domain.

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  • 33.
    Jin, Di
    et al.
    Technical University Darmstadt, Germany.
    Yin, Feng
    Ericsson AB, Linköping, Sweden.
    Fritsche, Carsten
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Zoubir, Abdelhak M.
    Technical University Darmstadt, Germany.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Cooperative localization based on severely quantized RSS measurements in wireless sensor network2016Ingår i: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Institute of Electrical and Electronics Engineers (IEEE), 2016, s. 4214-4218Konferensbidrag (Refereegranskat)
    Abstract [en]

    We study severely quantized received signal strength (RSS)-based cooperative localization in wireless sensor networks. We adopt the well-known sum-product algorithm over a wireless network (SPAWN) framework in our study. To address the challenge brought by severely quantized measurements, we adopt the principle of importance sampling and design appropriate proposal distributions. Moreover, we propose a parametric SPAWN in order to reduce both the communication overhead and the computational complexity. Experiments with real data corroborate that the proposed algorithms can achieve satisfactory localization accuracy for severely quantized RSS measurements. In particular, the proposed parametric SPAWN outperforms its competitors by far in terms of communication cost. We further demonstrate that knowledge about non-connected sensors can further improve the localization accuracy of the proposed algorithms.

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  • 34.
    Braga, André Ribeiro
    et al.
    Division of Electronics Engineering, Aeronautics Institute of Technology, São José dos Campos, Brazil.
    Fritsche, Carsten
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Bruno, Marcelo G. S.
    Division of Electronics Engineering, Aeronautics Institute of Technology, São José dos Campos, Brazil.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Cooperative Navigation and Coverage Identification with Random Gossip and Sensor Fusion2016Ingår i: Proc. IEEE 9th Sensor Array and Multichannel Signal Processing Workshop (SAM), Institute of Electrical and Electronics Engineers (IEEE), 2016, s. 1-5Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper is concerned with cooperative Terrain Aided Navigation of a network of aircraft using fusion of Radar Altimeter and inter-node range measurements. State inference is performed using a Rao-Blackwellized Particle Filter with online measurement noise statistics estimation. For terrain coverage measurement noise parameter identification, an online Expectation Maximization algorithm is proposed, where local sufficient statistics at each node are calculated in the E-step, which are then distributed to neighboring nodes using a random gossip algorithm to perform the M-step at each node. Simulation results show that improvement on positioning and calibration performance can be achieved compared to a non-cooperative approach.

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    fulltext
  • 35.
    Wan, Yiming
    et al.
    Delft University of Technology, Netherlands.
    Keviczky, Tamas
    Delft University of Technology, Netherlands.
    Verhaegen, Michel
    Delft University of Technology, Netherlands.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Data-driven robust receding horizon fault estimation2016Ingår i: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 71, s. 210-221Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This paper presents a data-driven receding horizon fault estimation method for additive actuator and sensor faults in unknown linear time-invariant systems, with enhanced robustness to stochastic identification errors. State-of-the-art methods construct fault estimators with identified state-space models or Markov parameters, without compensating for identification errors. Motivated by this limitation, we first propose a receding horizon fault estimator parameterized by predictor Markov parameters. This estimator provides (asymptotically) unbiased fault estimates as long as the subsystem from faults to outputs has no unstable transmission zeros. When the identified Markov parameters are used to construct the above fault estimator, stochastic identification errors appear as model uncertainty multiplied with unknown fault signals and online system inputs/outputs (I/O). Based on this fault estimation error analysis, we formulate a mixed-norm problem for the offline robust design that regards online I/O data as unknown. An alternative online mixed-norm problem is also proposed that can further reduce estimation errors at the cost of increased computational burden. Based on a geometrical interpretation of the two proposed mixed-norm problems, systematic methods to tune the user-defined parameters therein are given to achieve desired performance trade-offs. Simulation examples illustrate the benefits of our proposed methods compared to recent literature. (C) 2016 Elsevier Ltd. All rights reserved.

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    fulltext
  • 36.
    Bianco, Giuseppe
    et al.
    Lund University, Sweden.
    Ilieva, Mihaela
    Lund University, Sweden; Bulgarian Academic Science, Bulgaria.
    Veibäck, Clas
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Öfjäll, Kristoffer
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Gadomska, Alicja
    Lund University, Sweden.
    Hendeby, Gustaf
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Felsberg, Michael
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Åkesson, Susanne
    Lund University, Sweden.
    Emlen funnel experiments revisited: methods update for studying compass orientation in songbirds2016Ingår i: Ecology and Evolution, ISSN 2045-7758, E-ISSN 2045-7758, Vol. 6, nr 19, s. 6930-6942Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    1 Migratory songbirds carry an inherited capacity to migrate several thousand kilometers each year crossing continental landmasses and barriers between distant breeding sites and wintering areas. How individual songbirds manage with extreme precision to find their way is still largely unknown. The functional characteristics of biological compasses used by songbird migrants has mainly been investigated by recording the birds directed migratory activity in circular cages, so-called Emlen funnels. This method is 50 years old and has not received major updates over the past decades. The aim of this work was to compare the results from newly developed digital methods with the established manual methods to evaluate songbird migratory activity and orientation in circular cages. 2 We performed orientation experiments using the European robin (Erithacus rubecula) using modified Emlen funnels equipped with thermal paper and simultaneously recorded the songbird movements from above. We evaluated and compared the results obtained with five different methods. Two methods have been commonly used in songbirds orientation experiments; the other three methods were developed for this study and were based either on evaluation of the thermal paper using automated image analysis, or on the analysis of videos recorded during the experiment. 3 The methods used to evaluate scratches produced by the claws of birds on the thermal papers presented some differences compared with the video analyses. These differences were caused mainly by differences in scatter, as any movement of the bird along the sloping walls of the funnel was recorded on the thermal paper, whereas video evaluations allowed us to detect single takeoff attempts by the birds and to consider only this behavior in the orientation analyses. Using computer vision, we were also able to identify and separately evaluate different behaviors that were impossible to record by the thermal paper. 4 The traditional Emlen funnel is still the most used method to investigate compass orientation in songbirds under controlled conditions. However, new numerical image analysis techniques provide a much higher level of detail of songbirds migratory behavior and will provide an increasing number of possibilities to evaluate and quantify specific behaviors as new algorithms will be developed.

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    fulltext
  • 37.
    Sundqvist, Jacob
    et al.
    Combitech, Linköping, Sweden.
    Ekskog, Jonas
    Combitech, Linköping, Sweden.
    Dil, Bram
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Tordenlid, Jesper
    Combitech, Linköping, Sweden.
    Petterstedt, Michael
    Combitech, Linköping, Sweden.
    Feasibility Study on Smartphone Localization using Mobile Anchors in Search and Rescue Operations2016Ingår i: Proceedings of 19th International Conference on Sensor Fusion, Institute of Electrical and Electronics Engineers (IEEE), 2016, s. 1448-1453Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper presents a feasibility study on smartphone localization of missing persons in Search And Rescue(SAR) operations using widely available Commercial-Off-The-Shelf (COTS) products. We assume (1) that the missing person wears an enabled smartphone and (2) that messages transmitted by this smartphone can be intercepted by mobile agents at known positions. We present a proof-of-concept that consists of several mobile agents carrying smartphones that measure the Received Signal Strength (RSS) of Wi-fi messages transmitted by the smartphone of the missing person. The positions of the mobile agents are determined using the GPS unit on the smartphones.The mobile agents send the collected RSS and GPS data to a central processing unit, which processes the data in real-time and guides mobile agents in SAR operations by estimating the position of the missing person. Our central processing unit runs a localization algorithm that requires no calibration, because rescue operations usually take place in unknown environments with unknown hardware. Our experiments in an 250x130m2 outdoor field shows that our localization system provides an average localization performance of roughly 15 meters, which is sufficient for most SAR operations of interest. In addition, we performed several successful tests with a Quadcopter to show the feasibility of using unmanned vehicles in SAR operations.

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    fulltext
  • 38.
    Radnosrati, Kamiar
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Fritsche, Carsten
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Hendeby, Gustaf
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Gunnarsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten. Ericsson Research, Linköping, Sweden.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Fusion of TOF and TDOA for 3GPP Positioning2016Ingår i: Fusion 2016, 19th International Conference on Information Fusion: Proceedings, Institute of Electrical and Electronics Engineers (IEEE), 2016, s. 1454-1460Konferensbidrag (Refereegranskat)
    Abstract [en]

    Positioning in cellular networks is often based on mobile-assisted measurements of serving and neighboring base stations. Traditionally, positioning is considered to be enabled when the mobile provides measurements of three different base stations. In this paper, we additionally investigate positioning based on time series of Time Of Flight (TOF) and Time Difference of Arrival (TDOA) measurements gathered from two base stations with known positions, where the specific base stations involved depend on the trajectory of the mobile station.. The set of two base stations is different along the trajectory. Each report contains TOF for the serving base station, and one TDOA measurement for the most favorable neighboring base station relative the serving base station. We derive explicit analytical solution related to the intersection of the absolute distance circle (from TOF) and relative distance hyperbola (from TDOA). We consider both geometric noise-free problem and the more realistic problem with additive noise as delivered in the 3rd Generation Partnership Project (3GPP) Long-Term Evolution (LTE). Positioning performance is evaluated using the Cramer-Rao lower bound.

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    fulltext
  • 39.
    Kasebzadeh, Parinaz
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Fritsche, Carsten
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Hendeby, Gustaf
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Gunnarsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten. Ericsson Research, Linköping, Sweden.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Improved Pedestrian Dead Reckoning Positioning With Gait Parameter Learning2016Ingår i: Proceedings of the 19th International Conference on Information Fusion, IEEE conference proceedings, 2016, , s. 7s. 379-385Konferensbidrag (Refereegranskat)
    Abstract [en]

    We consider personal navigation systems in devices equipped with inertial sensors and GPS, where we propose an improved Pedestrian Dead Reckoning (PDR) algorithm that learns gait parameters in time intervals when position estimates are available, for instance from GPS or an indoor positioning system (IPS). A novel filtering approach is proposed that is able to learn internal gait parameters in the PDR algorithm, such as the step length and the step detection threshold. Our approach is based on a multi-rate Kalman filter bank that estimates the gait parameters when position measurements are available, which improves PDR in time intervals when the position is not available, for instance when passing from outdoor to indoor environments where IPS is not available. The effectiveness of the new approach is illustrated on several real world experiments. 

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    fulltext
  • 40.
    Nurminen, Henri
    et al.
    Tampere University of Technology, Department of Automation Science and Engineering, Finland.
    Rui, Rafael
    Universidade Federal do Rio Grande do Sul, Brazil.
    Ardeshiri, Tohid
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Bazanella, Alexandre
    Universidade Federal do Rio Grande do Sul Porto Alegre, Rio Grande do Sul, Brazil.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Mean and covariance matrix of a multivariate normal distribution with one doubly-truncated component2016Rapport (Övrigt vetenskapligt)
    Abstract [en]

    This technical report gives analytical formulas for the mean and covariancematrix of a multivariate normal distribution with one componenttruncated from both below and above.

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    doubly-truncated
  • 41.
    Roth, Michael
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Hendeby, Gustaf
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Nonlinear Kalman Filters Explained: A Tutorial on Moment Computations and Sigma Point Methods2016Ingår i: Journal of Advances in Information Fusion, ISSN 1557-6418, Vol. 11, nr 1, s. 47-70Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Nonlinear Kalman filters are algorithms that approximately solve the Bayesian filtering problem by employing the measurement update of the linear Kalman filter (KF). Numerous variants have been developed over the past decades, perhaps most importantly the popular sampling based sigma point Kalman filters.In order to make the vast literature accessible, we present nonlinear KF variants in a common framework that highlights the computation of mean values and covariance matrices as the main challenge. The way in which these moment integrals are approximated distinguishes, for example, the unscented KF from the divided difference KF.With the KF framework in mind, a moment computation problem is defined and analyzed. It is shown how structural properties can be exploited to simplify its solution. Established moment computation methods, and their basics and extensions, are discussed in an extensive survey. The focus is on the sampling based rules that are used in sigma point KF. More specifically, we present three categories of methods that use sigma-points 1) to represent a distribution (as in the UKF); 2) for numerical integration (as in Gauss-Hermite quadrature); 3) to approximate nonlinear functions (as in interpolation). Prospective benefits and downsides are listed for each of the categories and methods, including accuracy statements. Furthermore, the related KF publications are listed.The theoretical discussion is complemented with a comparative simulation study on instructive examples.

  • 42.
    Veibäck, Clas
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Hendeby, Gustaf
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    On Fusion of Sensor Measurements and Observation with Uncertain Timestamp for Target Tracking2016Ingår i: Proceedings of the 19th International Conference on Information Fusion, Institute of Electrical and Electronics Engineers (IEEE), 2016, s. 1268-1275Konferensbidrag (Refereegranskat)
    Abstract [en]

    We consider a target tracking problem where, in addition to the usual sensor measurements, accurate observations with uncertain timestamps are available. Such observations could, \eg, come from traces left by a target or from witnesses of an event, and have the potential in some scenarios to improve the accuracy of an estimate significantly. The Bayesian solution to the smoothing problem for one observation with uncertain timestamp is derived for a linear Gaussian state space model. The joint and marginal distributions of the states and uncertain time are derived, as well as the minimum mean squared error (MMSE) and maximum a posteriori (MAP) estimators. To attain an intuition for the problem in consideration a simple first-order example is presented and its posterior distributions and point estimators are compared and examined in some depth.

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    fulltext
  • 43.
    Nyqvist, Hanna E.
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Hendeby, Gustaf
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    On Joint Range and Velocity Estimation in Detection and Ranging Sensors2016Ingår i: Proceedings of 19th International Conference on Information Fusion (FUSION), Institute of Electrical and Electronics Engineers (IEEE), 2016, s. 1674-1681Konferensbidrag (Refereegranskat)
    Abstract [en]

    Radar and sonar provide information of both range and radial velocity to unknown objects. This is accomplished by emitting a signal waveform and computing the round trip time and Doppler shift. Estimation of the round trip time and Doppler shift is usually done separately without considering the couplings between these two object related quantities. The purpose of this contribution is to first model the amplitude, time shift and time scale of the returned signal in terms of the object related states range and velocity, and analyse the Cramér-Rao lower bound (CRLB) for the joint range and velocity estimation problem. One of the conclusions is that there is negative correlation between range and velocity. The maximum likelihood (ML) cost function also confirms this strong negative correlation. For target tracking applications, the use of the correct covariance matrix for the measurement vector gives a significant gain in information, compared to using the variance of range and velocity assuming independence. In other words, the knowledge of the correlation tells the tracking filter that a too large range measurement comes most likely with a too small velocity measurement, and vice versa. Experiments with sound pulses reflected in a wall indoors confirm the main conclusion of negative correlation.

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    fulltext
  • 44.
    Fritsche, Carsten
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Orguner, Umut
    Middle East Technical University, Turkey.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    On parametric lower bounds for discrete-time filtering2016Ingår i: 2015 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Institute of Electrical and Electronics Engineers (IEEE), 2016, s. 4338-4342Konferensbidrag (Refereegranskat)
    Abstract [en]

    Parametric Cramer-Rao lower bounds (CRLBs) are given for discrete-time systems with non-zero process noise. Recursive expressions for the conditional bias and mean-square-error (MSE) (given a specific state sequence) are obtained for Kalman filter estimating the states of a linear Gaussian system. It is discussed that Kalman filter is conditionally biased with a non-zero process noise realization in the given state sequence. Recursive parametric CRLBs are obtained for biased estimators for linear state estimators of linear Gaussian systems. Simulation studies are conducted where it is shown that Kalman filter is not an efficient estimator in a conditional sense.

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    fulltext
  • 45.
    Fritsche, Carsten
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Orguner, Umut
    Middle East Technical University, Turkey.
    Svensson, Lennart
    Chalmers University of Technology, Sweden.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Recent Results on Bayesian Cramer-Rao Bounds for Jump Markov Systems2016Ingår i: Proc. 19th International Conference on Information Fusion (FUSION), Institute of Electrical and Electronics Engineers (IEEE), 2016, s. 512-520Konferensbidrag (Refereegranskat)
    Abstract [en]

    In this paper, recent results on the evaluation of the Bayesian Cramer-Rao bound for jump Markov systems are presented. In particular, previous work is extended to jump Markov systems where the discrete mode variable enters into both the process and measurement equation, as well as where it enters exclusively into the measurement equation. Recursive approximations are derived with finite memory requirements as well as algorithms for checking the validity of these approximations are established. The tightness of the bound and the validity of its approximation is investigated on a couple of examples.

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    fulltext
  • 46.
    Lindfors, Martin
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Hendeby, Gustaf
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Gustafsson, Fredrik
    Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Institutionen för systemteknik, Reglerteknik.
    Karlsson, Rickard
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Vehicle Speed Tracking Using Chassis Vibrations2016Ingår i: Proceedings of the 2016 IEEE Intelligent Vehicles Symposium (IV), IEEE conference proceedings, 2016, s. 214-219Konferensbidrag (Refereegranskat)
    Abstract [en]

    The speed of a wheeled vehicle is usually estimatedusing wheel speed sensors (WSS) or GPS. If these signals are unavailable, other methods must be used. We propose a novelapproach exploiting the fact that vibrations from rotating axles,with fundamental frequency proportional to vehicle speed, aretransmitted via the vehicle chassis. Using an accelerometer, these vibrations can be tracked to estimate vehicle speed whileother sources of vibrations act as disturbances. A state-space model for the dynamics of the harmonics is presented andformulated such that there is a conditional linear-Gaussiansubstructure, enabling efficient Rao-Blackwellized methods. Avariant of the Rao-Blackwellized point-mass filter is derived, significantly reducing computational complexity, and reducingthe memory requirements from quadratic to linear in thenumber of grid points. It is applied to experimental data from the sensor cluster of a car and validated using therotational frequency from WSS data. The proposed methodshows improved performance and robustness in comparisonto a Rao-Blackwellized particle filter implementation and afrequency spectrum maximization method.

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    fulltext
  • 47.
    Nurminen, Henri
    et al.
    Tampere University of Technology, Finland.
    Ardeshiri, Tohid
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Piche, Robert
    Tampere University of Technology, Finland.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    A NLOS-robust TOA positioning filter based on a skew-t measurement noise model2015Ingår i: 2015 INTERNATIONAL CONFERENCE ON INDOOR POSITIONING AND INDOOR NAVIGATION (IPIN), IEEE , 2015Konferensbidrag (Refereegranskat)
    Abstract [en]

    A skew-t variational Bayes filter (STVBF) is applied to indoor positioning with time-of-arrival (TOA) based distance measurements and pedestrian dead reckoning (PDR). The proposed filter accommodates large positive outliers caused by occasional non-line-of-sight (NLOS) conditions by using a skew-t model of measurement errors. Real-data tests using the fusion of inertial sensors based PDR and ultra-wideband based TOA ranging show that the STVBF clearly outperforms the extended Kalman filter (EKF) in positioning accuracy with the computational complexity about three times that of the EKF.

  • 48.
    Ardeshiri, Tohid
    et al.
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Özkan, Emre
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Orguner, Umut
    Department of Electrical and Electronics Engineering, Middle East Technical University, Ankara, Turkey.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Approximate Bayesian Smoothing with Unknown Process and Measurement Noise Covariances2015Ingår i: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 22, nr 12, s. 2450-2454Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    We present an adaptive smoother for linear state-space models with unknown process and measurement noise covariances. The proposed method utilizes the variational Bayes technique to perform approximate inference. The resulting smoother is computationally efficient, easy to implement, and can be applied to high dimensional linear systems. The performance of the algorithm is illustrated on a target tracking example.

  • 49.
    Yin, Feng
    et al.
    Technical University of Darmstadt, Germany.
    Fritsche, Carsten
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Jin, Di
    Technical University of Darmstadt, Germany.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
    Zoubir, Abdelhak M.
    Technical University of Darmstadt, Germany.
    Cooperative Localization in WSNs Using Gaussian Mixture Modeling: Distributed ECM Algorithms2015Ingår i: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 63, nr 6, s. 1448-1463Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    We study cooperative sensor network localization in a realistic scenario where 1) the underlying measurement errors more probably follow a non-Gaussian distribution; 2) the measurement error distribution is unknown without conducting massive offline calibrations; and 3) non-line-of-sight identification is not performed due to the complexity constraint and/or storage limitation. The underlying measurement error distribution is approximated parametrically by a Gaussian mixture with finite number of components, and the expectation-conditional maximization (ECM) criterion is adopted to approximate the maximum-likelihood estimator of the unknown sensor positions and an extra set of Gaussian mixture model parameters. The resulting centralized ECM algorithms lead to easier inference tasks and meanwhile retain several convergence properties with a proof of the "space filling" condition. To meet the scalability requirement, we further develop two distributed ECM algorithms where an average consensus algorithm plays an important role for updating the Gaussian mixture model parameters locally. The proposed algorithms are analyzed systematically in terms of computational complexity and communication overhead. Various computer based tests are also conducted with both simulation and experimental data. The results pin down that the proposed distributed algorithms can provide overall good performance for the assumed scenario even under model mismatch, while the existing competing algorithms either cannot work without the prior knowledge of the measurement error statistics or merely provide degraded localization performance when the measurement error is clearly non-Gaussian.

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    fulltext
  • 50.
    Braga, André R.
    et al.
    Aeronautics Institute of Technology, Brazil.
    Bruno, Marcelo G.S.
    Aeronautics Institute of Technology, Brazil.
    Özkan, Emre
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Fritsche, Carsten
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Gustafsson, Fredrik
    Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska fakulteten.
    Cooperative Terrain Based Navigation and Coverage Identification Using Consensus2015Ingår i: 18th International Conference on Information Fusion (Fusion), 2015: Proceedings, IEEE , 2015, s. 1190-1197Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper presents a distributed online method for joint state and parameter estimation in a Jump Markov NonLinear System based on a distributed recursive Expectation Maximization algorithm. State inference is enabled via the use of Rao-Blackwellized Particle Filter and, for the parameter estimation, the E-step is performed independently at each sensor with the calculation of local sufficient statistics. An average consensus algorithm is used to diffuse local sufficient statistics to neighbors and approximate the global sufficient statistics throughout the network. The evaluation of the proposed algorithm is carried out on a Terrain Based Navigation problem where the unknown parameters of the observation noise model contain relevant information about the terrain properties.

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