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  • 51.
    Lindgren, David
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Hendeby, Gustaf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Distributed localization using acoustic Doppler2015In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 107, p. 43-53Article in journal (Refereed)
    Abstract [en]

     It is well-known that the motion of an acoustic source can be estimated from Doppler shift observations. It is however not obvious how to design a sensor network to efficiently deliver the localization service. In this work a rather simplistic motion model is proposed that is aimed at sensor networks with realistic numbersof sensor nodes. It is also described how to efficiently solve the associated least squares optimization problem by Gauss-Newton variable projection techniques, and how to initiate the numerical search from simple features extracted from the observed frequency series. The methods are evaluated by Monte Carlo simulations and demonstrated on real data by localizing an all-terrain vehicle. Itis concluded that the processing components included are fairly mature for practical implementations in sensor networks.

  • 52.
    Nilsson, Martin
    et al.
    Swedish Defence Research Agency (FOI), Linköping, Sweden.
    Rantakokko, Jouni
    Swedish Defence Research Agency (FOI), Linköping, Sweden; KTH Royal Institute of Technology, Stockholm, Sweden.
    Skoglund, Martin A.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Hendeby, Gustaf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. Swedish Defence Research Agency (FOI), Linköping, Sweden.
    Indoor Positioning Using Multi-Frequency RSS with Foot-Mounted INS2014In: Fifth International Conference on Indoor Positioning and Indoor Navigation, Institute of Electrical and Electronics Engineers (IEEE), 2014Conference paper (Refereed)
    Abstract [en]

    This paper presents a system which combines a zero-velocity-update-(ZUPT-)aided inertial navigation system (INS), using a foot-mounted inertial measurement unit (IMU), with opportunistic use of multi-frequency received signal strength (RSS) measurements. The system does not rely on maps or pre-collected data from surveys of the radio-frequency (RF) environment. Instead it builds its own database of collected RSS measurements during the course of the operation. New RSS measurements are continuously compared with the stored values in the database, and when the user returns to a previously visited area this can thus be detected. This enables loop-closures to be detected online and used for error drift correction. The system utilises a distributed particle simultaneous localization and mapping (DP-SLAM) algorithm which provides a flexible 2D navigation platform that can be extended with more sensors. The experimental results presented in this paper indicates that the developed RSS SLAM algorithm can, in many cases, significantly improve the positioning performance of a foot-mounted INS. 

  • 53.
    Nyqvist, Hanna E.
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Hendeby, Gustaf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    On Joint Range and Velocity Estimation in Detection and Ranging Sensors2016In: Proceedings of 19th International Conference on Information Fusion (FUSION), Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 1674-1681Conference paper (Refereed)
    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.

  • 54.
    Nyqvist, Hanna E.
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Skoglund, Martin A.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Hendeby, Gustaf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Pose Estimation Using Monocular Vision and Inertial Sensors Aided with Ultra Wide Band2015In: International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2015, IEEE , 2015Conference paper (Refereed)
    Abstract [en]

    This paper presents a method for global pose estimation using inertial sensors, monocular vision, and ultra wide band (UWB) sensors. It is demonstrated that the complementary characteristics of these sensors can be exploited to provide improved global pose estimates, without requiring the introduction of any visible infrastructure, such as fiducial markers. Instead, natural landmarks are jointly estimated with the pose of the platform using a simultaneous localization and mapping framework, supported by a small number of easy-to-hide UWB beacons with known positions. The method is evaluated with data from a controlled indoor experiment with high precision ground truth. The results show the benefit of the suggested sensor combination and suggest directions for further work.

  • 55. Olofsson, Jonatan
    et al.
    Veibäck, Clas
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Hendeby, Gustaf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Johansen, Tor Arne
    Outline of a System for Integrated Adaptive Ice Tracking and Multi-Agent Path Planning2017In: Proceedings of the 2017 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS), IEEE, 2017, p. 13-18Conference paper (Refereed)
    Abstract [en]

    In polar region operations, drift sea ice positioning and tracking is useful for both scientific and safety reasons. Modeling ice movements has proven difficult, not least due to the lack of information of currents and winds of high enough resolution. Thus, observations of drift ice is essential to an up-to-date ice-tracking estimate.

    Recent years have seen the rise of Unmanned Aerial Systems (UAS) as a platform for geoobservation, and so too for the tracking of sea ice. Being a mobile platform, the research on UAS path-planning is extensive and usually involves an objective-function to minimize. For the purpose of observation however, the objective-function typically changes as observations are made along the path.

    Further, the general problem involves multiple UAS and—in the case of sea ice tracking—vast geographical areas.

    In this paper we discuss the architectural outline of a system capable of fusing data from multiple sources—UAS’s and others—as well as incorporating that data for both path-planning, sea ice movement prediction and target initialization. The system contains tracking of sea ice objects, situation map logic and is expandable as discussed with path-planning capabilities for closing the loop of optimizing paths for information acquisition.

  • 56.
    Radnosrati, Kamiar
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Fritsche, Carsten
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Hendeby, Gustaf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Gunnarsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering. Ericsson Research, Linköping, Sweden.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Fusion of TOF and TDOA for 3GPP Positioning2016In: Fusion 2016, 19th International Conference on Information Fusion: Proceedings, Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 1454-1460Conference paper (Refereed)
    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.

  • 57.
    Reiss, Attila
    et al.
    German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany .
    Hendeby, Gustaf
    German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany .
    Bleser, Gabriele
    German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany .
    Stricker, Didier
    German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany .
    Activity Recognition Using Biomechanical Model Based Pose Estimation2010In: Smart Sensing and Context, 2010 / [ed] Paul Lukowicz, Kai Kunze, Gerd Kortuem, Springer Berlin/Heidelberg, 2010, p. 42-55Conference paper (Refereed)
    Abstract [en]

    In this paper, a novel activity recognition method based on signal-oriented and model-based features is presented. The model-based features are calculated from shoulder and elbow joint angles and torso orientation, provided by upper-body pose estimation based on a biomechanical body model. The recognition performance of signal-oriented and model-based features is compared within this paper, and the potential of improving recognition accuracy by combining the two approaches is proved: the accuracy increased by 4–6% for certain activities when adding model-based features to the signal-oriented classifier. The presented activity recognition techniques are used for recognizing 9 everyday and fitness activities, and thus can be applied for e.g., fitness applications or ‘in vivo’ monitoring of patients.

  • 58.
    Reiss, Attila
    et al.
    German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany.
    Hendeby, Gustaf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Stricker, Didier
    German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany.
    A Competitive Approach for Human Activity Recognition on Smartphones2013In: ESANN 2013, ESANN , 2013, p. 455-460Conference paper (Refereed)
    Abstract [en]

    This paper describes a competitive approach developed for an activity recognition challenge. The competition was defined on a new and publicly available dataset of human activities, recorded with smartphone sensors. This work investigates different feature sets for the activity recognition task of the competition. Moreover, the focus is also on the introduction of a new, confidence-based boosting algorithm called ConfAda- Boost.M1. Results show that the new classification method outperforms commonly used classifiers, such as decision trees or AdaBoost.M1.

  • 59.
    Reiss, Attila
    et al.
    University of Passau, Passau, Germany.
    Hendeby, Gustaf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. Swedish Def Res Agcy FOI, Dept Sensor & EW Syst, Linkoping, Sweden.
    Stricker, Didier
    German Research Center for Artificial Intelligence, Kaiserslautern, Germany.
    A novel confidence-based multiclass boosting algorithm for mobile physical activity monitoring2015In: Personal and Ubiquitous Computing, ISSN 1617-4909, E-ISSN 1617-4917, Vol. 19, no 1, p. 105-121Article in journal (Refereed)
    Abstract [en]

    This paper addresses one of the main challenges in physical activity monitoring, as indicated by recent benchmark results: The difficulty of the complex classification problems exceeds the potential of existing classifiers. Therefore, this paper proposes the ConfAdaBoost.M1 algorithm. This algorithm is a variant of the AdaBoost.M1 that incorporates well-established ideas for confidence-based boosting. ConfAdaBoost.M1 is compared to the most commonly used boosting methods using benchmark datasets from the UCI machine learning repository.  Moreover, it is evaluated on an activity recognition and an intensity estimation problem, including a large number of physical activities from the recently released PAMAP2 dataset. The presented results indicate that the proposed ConfAdaBoost.M1 algorithm significantly improves the classification performance on most of the evaluated datasets, especially for larger and more complex classification tasks. Finally, two empirical studies are designed and carried out to investigate the feasibility of ConfAdaBoost.M1 for physical activity monitoring applications in mobile systems.

  • 60.
    Reiss, Attila
    et al.
    German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany.
    Hendeby, Gustaf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Stricker, Didier
    German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany.
    Confidence-based multiclass AdaBoost for physical activity monitoring2013In: ISWC '13: Proceedings of the 2013 International Symposium on Wearable Computers, 2013, p. 13-20Conference paper (Refereed)
    Abstract [en]

    Physical activity monitoring has recently become an important topic in wearable computing, motivated by e.g. healthcare applications. However, new benchmark results show that the difficulty of the complex classification problems exceeds the potential of existing classifiers. Therefore, this paper proposes the ConfAdaBoost.M1 algorithm. The proposed algorithm is a variant of the AdaBoost.M1 that incorporates well established ideas for confidence based boosting. The method is compared to the most commonly used boosting methods using benchmark datasets from the UCI machine learning repository and it is also evaluated on an activity recognition and an intensity estimation problem, including a large number of physical activities from the recently released PAMAP2 dataset. The presented results indicate that the proposed ConfAdaBoost.M1 algorithm significantly improves the classification performance on most of the evaluated datasets, especially for larger and more complex classification tasks.

  • 61.
    Reiss, Attila
    et al.
    German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany.
    Hendeby, Gustaf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Stricker, Didier
    German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany.
    Towards Robust Activity Recognition for Everyday Life: Methods and Evaluation2013In: 7th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), 2013, IEEE , 2013, p. 25-32Conference paper (Refereed)
    Abstract [en]

    The monitoring of physical activities under realistic, everyday life conditions - thus while an individual follows his regular daily routine - is usually neglected or even completely ignored. Therefore, this paper investigates the development and evaluation of robust methods for everyday life scenarios, with focus on the task of aerobic activity recognition. Two important aspects of robustness are investigated: dealing with various (unknown) other activities and subject independency. Methods to handle these issues are proposed and compared, a thorough evaluation simulates usual everyday scenarios of the usage of activity recognition applications. Moreover, a new evaluation technique is introduced (leave-one-other-activity-out) to simulate when an activity recognition system is used while performing a previously unknown activity. Through applying the proposed methods it is possible to design a robust physical activity recognition system with the desired generalization characteristic.

  • 62.
    Roth, Michael
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Fritsche, Carsten
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Hendeby, Gustaf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering. Swedish Defence Research Agency (FOI), Linköping, Sweden.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    The Ensemble Kalman Filter and its Relations to Other Nonlinear Filters2015In: Proceedings of the 2015 European Signal Processing Conference (EUSIPCO 2015), Institute of Electrical and Electronics Engineers (IEEE), 2015, p. 1236-1240Conference paper (Refereed)
    Abstract [en]

    The Ensemble Kalman filter (EnKF) is a standard algorithm in oceanography and meteorology, where it has got thousands of citations. It is in these communities appreciated since it scales much better with state dimension n than the standard Kalman filter (KF). In short, the EnKF propagates ensembles with N state realizations instead of mean values and covariance matrices and thereby avoids the computational and storage burden of working on n×n matrices. Perhaps surprising, very little attention has been devoted to the EnKF in the signal processing community. In an attempt to change this, we present the EnKF in a Kalman filtering context. Furthermore, its application to nonlinear problems is compared to sigma point Kalman filters and the particle filter, so as to reveal new insights and improvements for high-dimensional filtering algorithms in general. A simulation example shows the EnKF performance in a space debris tracking application.

  • 63.
    Roth, Michael
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Hendeby, Gustaf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    EKF/UKF Maneuvering Target Tracking using Coordinated Turn Models with Polar/Cartesian Velocity2014In: 17th International Conference on Information Fusion (FUSION), 2014, Institute of Electrical and Electronics Engineers (IEEE), 2014, p. 1-8Conference paper (Refereed)
    Abstract [en]

    Nonlinear Kalman filter adaptations such as extended Kalman filters (EKF) or unscented Kalman filters (UKF) provide approximate solutions to state estimation problems in nonlinear models. The algorithms utilize mean values and covariance matrices to represent the probability densities in the otherwise intractable Bayesian filtering equations. As a consequence, their estimation performance can show significant dependence on the choice of state coordinates. The here considered problem of tracking maneuvering targets using coordinated turn (CT) models is one practically relevant example: The velocity in the target state can either be formulated in Cartesian or polar coordinates. We extend a previous study to a broader range of CT models that allow for changes in target speed and turn rate, and investigate UKF as well as EKF variants in terms of their performance and sensitivity to noise parameters. The results advocate for the use of polar CT models.

  • 64.
    Roth, Michael
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Hendeby, Gustaf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Nonlinear Kalman Filters Explained: A Tutorial on Moment Computations and Sigma Point Methods2016In: Journal of Advances in Information Fusion, ISSN 1557-6418, Vol. 11, no 1, p. 47-70Article in journal (Refereed)
    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.

  • 65.
    Saha, Saikat
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Hendeby, Gustaf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. Swedish Defence Research Agency (FOI), Linköping, Sweden.
    Rao-Blackwellized particle filter for Markov modulated nonlinear dynamic systems2014In: Statistical Signal Processing (SSP), 2014 IEEE Workshop on, IEEE , 2014, p. 272-275Conference paper (Refereed)
    Abstract [en]

    The Markov modulated (switching) state space is an important model paradigm in statistical signal processing. In this article, we specifically consider Markov modulated nonlinear state-space models and address the online Bayesian inference problem for such models. In particular, we propose a new Rao-Blackwellized particle filter for the inference task which is our main contribution here. A detailed description of the problem and an algorithm is presented.

  • 66.
    Saha, Saikat
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Hendeby, Gustaf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Mixture Kalman Filters and Beyond2015In: Current Trends in Bayesian Methodology with Applications / [ed] Satyanshu K. Upadhyay, Umesh Singh, Dipak K. Dey, Appaia Loganathan, Boca Raton: Chapman and Hall/CRC Press, Taylor & Frances , 2015, p. 537-562Chapter in book (Refereed)
    Abstract [en]

    The discrete time general state-space model is a flexible framework to deal with the nonlinear and/or non-Gaussian time series problems. However, the associated (Bayesian) inference problems are often intractable. Additionally, for many applications of interest, the inference solutions are required to be recursive over time. The particle filter (PF) is a popular class of Monte Carlo based numerical methods to deal with such problems in real time. However, PF is known to be computationally expensive and does not scale well with the problem dimensions. If a part of the state space is analytically tractable conditioned on the remaining part, the Monte Carlo based estimation is then confined to a space of lower dimension, resulting in an estimation method known as the Rao-Blackwellized particle filter (RBPF).

    In this chapter, we present a brief review of Rao-Blackwellized particle filtering. Especially, we outline a set of popular conditional tractable structures admitting such Rao-Blackwellization in practice. For some special and/or relatively new cases, we also provide reasonably detailed descriptions.We confine our presentation mostly to the practitioners’ point of view.

  • 67.
    Saha, Saikat
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Hendeby, Gustaf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Noise Adaptive Particle Filtering: A Hierarchical Perspective2013Conference paper (Other academic)
    Abstract [en]

    Optimal estimation problems for general state space models do not typically admit a closed form solution. However, modern Monte Carlo methods have paved the way to solve such complex inference problems. Particle filters (PF) are a popular class of such Monte Carlo based Bayesian algorithms, which solve the estimation problems numerically in a sequential manner.

    PF in general, assume a prior knowledge of the (process and observation) noise distributions involving the state space model, whereas the properties of the noise processes are often unknown for many practical problems. Furthermore, the unknown noise distributions may be state dependent or even non-stationary, which prevent the offline noise calibrations.

    In this article, the unknown noises are assumed to be slowly varying in time. The article then proposes a hierarchical noise adaptive PF where a two tier PF is run, the top tier PF estimates the latent states from the streaming observations and the bottom tier PF estimates the noise statistics conditioned on the top tier PF output together with the observations. The estimates are statistically fused together for the inference purpose. In essence, it is an implementation of approximate Rao-Blackwellized PF, where the later is achieved through local Monte Carlo integration. This approach is very generic for different noise classes and importantly, it enhances the level of parallelism in PF implementations.

  • 68.
    Skoglund, Martin
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Hendeby, Gustaf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Axehill, Daniel
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Extended Kalman Filter Modifications Based on an Optimization View Point2015In: 18th International Conference of Information Fusion, Institute of Electrical and Electronics Engineers (IEEE), 2015Conference paper (Refereed)
    Abstract [en]

    The extended Kalman filter (EKF) has been animportant tool for state estimation of nonlinear systems sinceits introduction. However, the EKF does not possess the same optimality properties as the Kalman filter, and may perform poorly. By viewing the EKF as an optimization problem it is possible to, in many cases, improve its performance and robustness. The paper derives three variations of the EKF by applying different optimisation algorithms to the EKF costfunction and relate these to the iterated EKF. The derived filters are evaluated in two simulation studies which exemplify the presented filters.

  • 69.
    Skoglund, Martin
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Hendeby, Gustaf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Nygårds, Jonas
    Swedish Defence Research Agency (FOI).
    Rantakokko, Jouni
    Swedish Defece Research Agency (FOI).
    Eriksson, Gunnar
    Swedish Defence Research Agency (FOI).
    Indoor Localization Using Multi-Frequency RSS2016In: Proceddings of the IEEE/ION Position Location and Navigation Symposium, IEEE conference proceedings, 2016, p. 177-186Conference paper (Refereed)
    Abstract [en]

    This paper investigates the usefulness of multi-frequency received signal strength (RSS) for indoor localization. Acollected set of data from four sites containing 7 frequencies fromdual receivers and a high accuracy reference positioning systemis presented. The collected data is also made publicly availablethrough ResearchGate. The data is analyzed with respect tospatial variations using Gaussian processes ( GP ). The resultsshow that there are more rapid signal variations across corridorsthan along them. The uniqueness of RSS fingerprints is analyzedsuggesting that sequences of measurements in smoothing, orsmoothing-like, algorithms that can handle temporary positionambiguities are likely the best choice for localization applications.

  • 70.
    Veibäck, Clas
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Hendeby, Gustaf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    On Fusion of Sensor Measurements and Observation with Uncertain Timestamp for Target Tracking2016In: Proceedings of the 19th International Conference on Information Fusion, Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 1268-1275Conference paper (Refereed)
    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.

  • 71.
    Veibäck, Clas
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Hendeby, Gustaf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Tracking of Dolphins in a Basin Using a Constrained Motion Model2015In: Proceedings of the 18th International Conference of Information Fusion, Institute of Electrical and Electronics Engineers (IEEE), 2015Conference paper (Refereed)
    Abstract [en]

    Visual animal tracking is a challenging problem generally requiring extended target models, group tracking and handling of clutter and missed detections. Furthermore, the dolphin tracking problem we consider includes basin constraints, shadows, limited field of view and rapidly changing light conditions. We describe the whole pipeline of a solution based on a ceiling-mounted fisheye camera that includes foreground segmentation and observation extraction in each image, followed by a target tracking framework. A novel contribution is a potential field model of the basin edges as a part of the motion model, that provides a robust prediction of the dolphin trajectories in phases with long segments of missed detections. The overall performance on real data is quite promising.

  • 72.
    Weber, Markus
    et al.
    German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany .
    Bleser, Gabriele
    German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany .
    Hendeby, Gustaf
    German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany .
    Reiss, Attila
    German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany .
    Stricker, Didier
    German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany .
    Unsupervised model generation for motion monitoring2011In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC): Workshop on Robust Machine Learning Techniques for Human Activity Recognition, IEEE , 2011Conference paper (Refereed)
    Abstract [en]

    This paper addresses two fundamental requirements of full body motion monitoring: (a) the ability to sense the input of the user and (b) the means to interpret the captured input. Appropriate technology in both areas is required for an interactive virtual reality system to provide feedback in a useful and natural way. This paper combines technologies for both areas: It develops a sensor fusion approach for capturing user input based on miniature on-body inertial and magnetic motion sensors. Furthermore, it presents work in progress to automatically generate models for motion patterns from the captured input. The technology is then used and evaluated in the context of a personalized virtual rehabilitation trainer application.

  • 73.
    Zhao, Yuxin
    et al.
    Ericsson Research, Linköping, Sweden.
    Yin, Feng
    Ericsson Research, Linköping, Sweden.
    Gunnarsson, Fredrik
    Ericsson Research, Linköping, Sweden.
    Amirijoo, Mehdi
    Ericsson Research, Linköping, Sweden.
    Hendeby, Gustaf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Gaussian Process for Propagation modeling and Proximity Reports Based Indoor Positioning2016In: 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring), IEEE , 2016, p. 1-5Conference paper (Refereed)
    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 network-based positioning based on times series of proximity reports from a mobile device, either only a proximity indicator, or a vector of RSS from observed nodes. Such positioning corresponds to a latent and nonlinear observation model. To address these problems, we combine two powerful tools, namely particle filtering and Gaussian process regression (GPR) for radio signal propagation modeling. The latter also provides some insights into the spatial correlation of the radio propagation in the considered area. Radio propagation modeling and positioning performance are evaluated in a typical office area with Bluetooth-Low-Energy (BLE) beacons deployed for proximity detection and reports. Results show that the positioning accuracy can be improved by using GPR.

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