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Fritsche, C., Orguner, U., Özkan, E. & Gustafsson, F. (2018). Marginal Bayesian Bhattacharyya Bounds for discrete-time filtering. In: Proc. of 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, Canada, 2018: . Paper presented at IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 10-20 April, Calgary, Canada, 2018 (pp. 4289-4293).
Open this publication in new window or tab >>Marginal Bayesian Bhattacharyya Bounds for discrete-time filtering
2018 (English)In: Proc. of 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, Canada, 2018, 2018, p. 4289-4293Conference paper, Published paper (Refereed)
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.

Keywords
Performance bounds, Bayesian estimation, Bhattacharyya bounds, nonlinear filtering, particle filter
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-148405 (URN)
Conference
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 10-20 April, Calgary, Canada, 2018
Funder
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Available from: 2018-06-08 Created: 2018-06-08 Last updated: 2018-06-14Bibliographically approved
Zhao, Y., Fritsche, C., Yin, F., Gunnarsson, F. & Gustafsson, F. (2018). Sequential Monte Carlo Methods and Theoretical Bounds for Proximity Report Based Indoor Positioning. IEEE Transactions on Vehicular Technology
Open this publication in new window or tab >>Sequential Monte Carlo Methods and Theoretical Bounds for Proximity Report Based Indoor Positioning
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2018 (English)In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018
Keywords
Proximity, indoor positioning, particle filtering and smoothing, Cramer-Rao lower bounds
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-147834 (URN)10.1109/TVT.2018.2799174 (DOI)2-s2.0-85041415767 (Scopus ID)
Available from: 2018-05-15 Created: 2018-05-15 Last updated: 2018-05-22Bibliographically approved
Fritsche, C. & Gustafsson, F. (2017). Bayesian Bhattacharyya bound for discrete-time filtering revisited. In: Proc. of 2017 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP): . Paper presented at 2017 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Curacao, Dutch Antilles, Dec. 10-13, 2017 (pp. 719-723).
Open this publication in new window or tab >>Bayesian Bhattacharyya bound for discrete-time filtering revisited
2017 (English)In: Proc. of 2017 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2017, p. 719-723Conference paper, Published paper (Refereed)
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

Keywords
Bhattacharyya bound, nonlinear filtering, mean square error inequality
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-144022 (URN)10.1109/CAMSAP.2017.8313201 (DOI)000428438100145 ()9781538612514 (ISBN)9781538612507 (ISBN)9781538612521 (ISBN)
Conference
2017 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Curacao, Dutch Antilles, Dec. 10-13, 2017
Funder
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Available from: 2018-01-03 Created: 2018-01-03 Last updated: 2018-06-11Bibliographically approved
Sjanic, Z., Skoglund, M. A. & Gustafsson, F. (2017). EM-SLAM with Inertial/Visual Applications. IEEE Transactions on Aerospace and Electronic Systems, 53(1), 273-285
Open this publication in new window or tab >>EM-SLAM with Inertial/Visual Applications
2017 (English)In: IEEE Transactions on Aerospace and Electronic Systems, ISSN 0018-9251, E-ISSN 1557-9603, Vol. 53, no 1, p. 273-285Article in journal (Refereed) Published
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. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017
Keywords
SLAM, Expectation-Maximisation, Sensor Fu- sion, Computer Vision, Inertial Sensors
National Category
Robotics
Identifiers
urn:nbn:se:liu:diva-110371 (URN)10.1109/TAES.2017.2650118 (DOI)000399934000022 ()
Note

Funding agencies: Vinnova Industry Excellence Center LINK-SIC

Available from: 2014-09-09 Created: 2014-09-09 Last updated: 2017-05-18Bibliographically approved
Braga, A. R., Fritsche, C., Bruno, M. G. S. & Gustafsson, F. (2017). Rapid System Identification for Jump Markov Non-Linear Systems. In: Proc. 2017 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP): . Paper presented at 2017 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Curacao, Dutch Antilles, Dec. 10-13, 2017 (pp. 714-718). IEEE
Open this publication in new window or tab >>Rapid System Identification for Jump Markov Non-Linear Systems
2017 (English)In: Proc. 2017 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), IEEE, 2017, p. 714-718Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
IEEE, 2017
Keywords
parameter estimation, system indentification, jump Markov systems, particle filtering
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-144023 (URN)10.1109/CAMSAP.2017.8313089 (DOI)000428438100033 ()9781538612514 (ISBN)9781538612507 (ISBN)9781538612521 (ISBN)
Conference
2017 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Curacao, Dutch Antilles, Dec. 10-13, 2017
Projects
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Available from: 2018-01-03 Created: 2018-01-03 Last updated: 2018-04-27Bibliographically approved
Karlsson, R. & Gustafsson, F. (2017). The Future of Automotive Localization Algorithms: Available, reliable, and scalable localization: Anywhere and anytime. IEEE signal processing magazine (Print), 34(2), 60-69
Open this publication in new window or tab >>The Future of Automotive Localization Algorithms: Available, reliable, and scalable localization: Anywhere and anytime
2017 (English)In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 34, no 2, p. 60-69Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-135786 (URN)10.1109/MSP.2016.2637418 (DOI)000397574300008 ()2-s2.0-85015356723 (Scopus ID)
Projects
Wallenberg Autonomous Systems Program
Note

Funding agencies: Wallenberg Autonomous Systems Program

Available from: 2017-03-22 Created: 2017-03-22 Last updated: 2017-04-21Bibliographically approved
Örn, D., Szilassy, M., Dil, B. & Gustafsson, F. (2016). A Novel Multi-Step Algorithm for Low-Energy Positioning Using GPS. In: Fusion 2016, 19th International Conference on Information Fusion: Proceedings. Paper presented at 19th International Conference on Information Fusion, Heidelberg, Germany, July 5-8, 2016 (pp. 1469-1476).
Open this publication in new window or tab >>A Novel Multi-Step Algorithm for Low-Energy Positioning Using GPS
2016 (English)In: Fusion 2016, 19th International Conference on Information Fusion: Proceedings, 2016, p. 1469-1476Conference paper, Published paper (Refereed)
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.

National Category
Control Engineering Communication Systems
Identifiers
urn:nbn:se:liu:diva-130236 (URN)000391273400195 ()9780996452748 (ISBN)
Conference
19th International Conference on Information Fusion, Heidelberg, Germany, July 5-8, 2016
Funder
Swedish Institute
Available from: 2016-07-22 Created: 2016-07-22 Last updated: 2018-03-20Bibliographically approved
Alickovic, E., Lunner, T. & Gustafsson, F. (2016). A System Identification Approach to Determining Listening Attention from EEG Signals. In: 2016 24TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO): . Paper presented at 24th European Signal Processing Conference (EUSIPCO), Aug 28-Sep 2, 2016. Budapest, Hungary (pp. 31-35). IEEE
Open this publication in new window or tab >>A System Identification Approach to Determining Listening Attention from EEG Signals
2016 (English)In: 2016 24TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO), IEEE , 2016, p. 31-35Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
IEEE, 2016
Series
European Signal Processing Conference, ISSN 2076-1465
Keywords
attention, cocktail party, linear regression (LR), finite impulse response (FIR), multivariable model, sound, EEG
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-130732 (URN)10.1109/EUSIPCO.2016.7760204 (DOI)000391891900007 ()978-0-9928-6265-7 (ISBN)978-1-5090-1891-8 (ISBN)
Conference
24th European Signal Processing Conference (EUSIPCO), Aug 28-Sep 2, 2016. Budapest, Hungary
Available from: 2016-08-22 Created: 2016-08-22 Last updated: 2017-02-15
Dil, B., Hendeby, G., Gustafsson, F. & Hoenders, B. (2016). Approximate Diagonalized Covariance Matrix for Signals with Correlated Noise. In: Proceedings of the 19th International Conference of Information Fusion: . Paper presented at 19th International Conference of Information Fusion, Heidelberg, Germany, July 5-8 2016 (pp. 521-527). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Approximate Diagonalized Covariance Matrix for Signals with Correlated Noise
2016 (English)In: Proceedings of the 19th International Conference of Information Fusion, Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 521-527Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016
Keywords
Signal processing, approximate estimation
National Category
Control Engineering Signal Processing
Identifiers
urn:nbn:se:liu:diva-130162 (URN)000391273400071 ()978-0-9964527-4-8 (ISBN)
Conference
19th International Conference of Information Fusion, Heidelberg, Germany, July 5-8 2016
Funder
Swedish Research Council
Available from: 2016-07-12 Created: 2016-07-12 Last updated: 2017-02-03Bibliographically approved
Jin, D., Yin, F., Fritsche, C., Zoubir, A. M. & Gustafsson, F. (2016). Cooperative localization based on severely quantized RSS measurements in wireless sensor network. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP): . Paper presented at IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China, 20-25 March, 2016 (pp. 4214-4218). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Cooperative localization based on severely quantized RSS measurements in wireless sensor network
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2016 (English)In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 4214-4218Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016
Series
International Conference on Acoustics Speech and Signal Processing ICASSP, ISSN 1520-6149
Keywords
Distributed cooperative localization, SPAWN, quantized RSS, wireless sensor network
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-128632 (URN)10.1109/ICASSP.2016.7472471 (DOI)000388373404072 ()978-1-4799-9988-0 (ISBN)
Conference
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China, 20-25 March, 2016
Available from: 2016-05-25 Created: 2016-05-25 Last updated: 2017-01-11
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-3270-171X

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