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Orguner, Umut
Publications (10 of 48) Show all publications
Ardeshiri, T., Özkan, E., Orguner, U. & Gustafsson, F. (2015). Variational Iterations for Smoothing with Unknown Process and Measurement Noise Covariances. Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Variational Iterations for Smoothing with Unknown Process and Measurement Noise Covariances
2015 (English)Report (Other academic)
Abstract [en]

In this technical report, some derivations for the smoother proposed in [1] are presented. More specifically, the derivations for the cyclic iteration needed to solve the variational Bayes smoother for linear state-space models with unknownprocess and measurement noise covariances in [1] are presented. Further, the variational iterations are compared with iterations of the Expectation Maximization (EM) algorithm for smoothing linear state-space models with unknown noise covariances.

[1] T. Ardeshiri, E. Özkan, U. Orguner, and F. Gustafsson, ApproximateBayesian smoothing with unknown process and measurement noise covariances, submitted to Signal Processing Letters, 2015.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2015. p. 12
Series
LiTH-ISY-R, ISSN 1400-3902 ; 3086
Keywords
Adaptive smoothing, variational Bayes, sensor calibration, Rauch-Tung-Striebel smoother, Kalman filtering, noise covariance
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-120700 (URN)LiTH-ISY-R-3086 (ISRN)
Available from: 2015-08-30 Created: 2015-08-21 Last updated: 2015-09-17Bibliographically approved
Ardeshiri, T., Özkan, E. & Orguner, U. (2013). On Reduction of Mixtures of the Exponential Family Distributions. Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>On Reduction of Mixtures of the Exponential Family Distributions
2013 (English)Report (Other academic)
Abstract [en]

Many estimation problems require a mixture reduction algorithm with which an increasing number of mixture components are reduced to a tractable level. In this technical report a discussion on dierent aspects of mixture reduction is given followed by a presentation of numerical simulation on reduction of mixture densities where the component density belongs to the exponential family of distributions.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2013. p. 48
Series
LiTH-ISY-R, ISSN 1400-3902 ; 3076
Keywords
Mixture density, mixture reduction, exponential family, integral square error, Kullback-Leibler divergence, Exponential Distribution, Weibull Distribution, Laplace Distribution, Rayleigh Distribution, Log-normal Distri- bution, Gamma Distribution, Inverse Gamma Distribution, Univariate Gaus- sian Distribution, Multivariate Gaussian Distribution, Gaussian Gamma Dis- tribution, Dirichlet distribution, Wishart Distribution, Inverse Wishart Dis- tribution, Gaussian Inverse Wishart Distribution.
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-100234 (URN)LiTH-ISY-R-3076 (ISRN)
Available from: 2013-10-31 Created: 2013-10-31 Last updated: 2014-08-18Bibliographically approved
Granström, K. & Orguner, U. (2013). On Spawning and Combination of Extended/Group Targets Modeled with Random Matrices. IEEE Transactions on Signal Processing, 61(3), 678-692
Open this publication in new window or tab >>On Spawning and Combination of Extended/Group Targets Modeled with Random Matrices
2013 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 61, no 3, p. 678-692Article in journal (Refereed) Published
Abstract [en]

In extended/group target tracking, where the extensions of the targets are estimated, target spawning and combination events might have significant implications on the extensions. This paper investigates target spawning and combination events for the case that the target extensions are modeled in a random matrix framework. The paper proposes functions that should be provided by the tracking filter in such a scenario. The results, which are obtained by a gamma Gaussian inverse Wishart implementation of an extended target probability hypothesis density filter, confirms that the proposed functions improve the performance of the tracking filter for spawning and combination events.

Place, publisher, year, edition, pages
IEEE Signal Processing Society, 2013
Keywords
Extended target, Random matrix, Kullback-Leibler divergence, Target spawning, Target combination
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-82005 (URN)10.1109/TSP.2012.2230171 (DOI)000314719100013 ()
Projects
CADICSETTCUAS
Funder
Swedish Research Council, 621-2010-4301Swedish Foundation for Strategic Research
Note

Funding Agencies|Swedish Research Council|621-2010-4301|Swedish Foundation for Strategic Research (SSF)||

Available from: 2012-09-27 Created: 2012-09-27 Last updated: 2017-12-07Bibliographically approved
Granström, K. & Orguner, U. (2012). A PHD Filter for Tracking Multiple Extended Targets using Random Matrices. IEEE Transactions on Signal Processing, 60(11), 5657-5671
Open this publication in new window or tab >>A PHD Filter for Tracking Multiple Extended Targets using Random Matrices
2012 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 60, no 11, p. 5657-5671Article in journal (Refereed) Published
Abstract [en]

This paper presents a random set based approach to tracking of an unknown number of extended targets, in the presence of clutter measurements and missed detections, where the targets extensions are modeled as random matrices. For this purpose, the random matrix framework developed recently by Koch et al. is adapted into the extended target PHD framework, resulting in the Gaussian inverse Wishart PHD (GIW-PHD) filter. A suitable multiple target likelihood is derived, and the main filter recursion is presented along with the necessary assumptions and approximations. The particularly challenging case of close extended targets is addressed with practical measurement clustering algorithms. The capabilities and limitations of the resulting extended target tracking framework are illustrated both in simulations and in experiments based on laser scans.

Place, publisher, year, edition, pages
IEEE Signal Processing Society, 2012
Keywords
Gaussian distribution, PHD filter, Target tracking, Extended target, Inverse Wishart distribution, Laser sensor, Occlusion, Probability of detection, Random matrix, Random set
National Category
Signal Processing Control Engineering
Identifiers
urn:nbn:se:liu:diva-82000 (URN)10.1109/TSP.2012.2212888 (DOI)000310139900004 ()
Projects
CADICSETTCUAS
Funder
Swedish Research Council, 621-2010-4301Swedish Foundation for Strategic Research
Note

funding agencies|Swedish Research Council|621-2010-4301|Foundation for Strategic Research (SSF)||

Available from: 2012-10-01 Created: 2012-09-27 Last updated: 2017-12-07Bibliographically approved
Orguner, U. (2012). A Variational Measurement Update for Extended Target Tracking With Random Matrices. IEEE Transactions on Signal Processing, 60(7), 3827-3834
Open this publication in new window or tab >>A Variational Measurement Update for Extended Target Tracking With Random Matrices
2012 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 60, no 7, p. 3827-3834Article in journal (Refereed) Published
Abstract [en]

This correspondence proposes a new measurement update for extended target tracking under measurement noise when the target extent is modeled by random matrices. Compared to the previous measurement update developed by Feldmann et al., this work follows a more rigorous path to derive an approximate measurement update using the analytical techniques of variational Bayesian inference. The resulting measurement update, though computationally more expensive, is shown via simulations to be better than the earlier method in terms of both the state estimates and the predictive likelihood for moderate amounts of prediction errors.

Place, publisher, year, edition, pages
IEEE Signal Processing Society, 2012
Keywords
Extended target tracking, Measurement update, Random matrices, Variational Bayes
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-79686 (URN)10.1109/TSP.2012.2192927 (DOI)000305578800039 ()
Available from: 2012-08-13 Created: 2012-08-13 Last updated: 2017-12-07
Granström, K., Lundquist, C. & Orguner, U. (2012). Extended Target Tracking Using a Gaussian-Mixture PHD Filter. IEEE Transactions on Aerospace and Electronic Systems, 48(4), 3268-3286
Open this publication in new window or tab >>Extended Target Tracking Using a Gaussian-Mixture PHD Filter
2012 (English)In: IEEE Transactions on Aerospace and Electronic Systems, ISSN 0018-9251, E-ISSN 1557-9603, Vol. 48, no 4, p. 3268-3286Article in journal (Refereed) Published
Abstract [en]

This paper presents a Gaussian-mixture implementation of the phd filter for tracking extended targets. The exact filter requires processing of all possible measurement set partitions, which is generally infeasible to implement. A method is proposed for limiting the number of considered partitions and possible alternatives are discussed. The implementation is used on simulated data and in experiments with real laser data, and the advantage of the filter is illustrated. Suitable remedies are given to handle spatially close targets and target occlusion.

Keywords
Target tracking, Extended target, PHD filter, Random set, Gaussian-mixture, Laser sensor
National Category
Signal Processing Control Engineering
Identifiers
urn:nbn:se:liu:diva-71866 (URN)10.1109/TAES.2012.6324703 (DOI)000309865600030 ()
Projects
CADICSETTCUAS
Funder
Swedish Foundation for Strategic Research Swedish Research Council
Available from: 2012-10-01 Created: 2011-11-08 Last updated: 2017-12-08Bibliographically approved
Burak Guldogan, M., Orguner, U. & Gustafsson, F. (2012). Gaussian mixture PHD filter for multi-target tracking using passive doppler-only measurements. In: IET Conference Publications: vol 2012, issue 595 CP. Paper presented at Data Fusion & Target Tracking Conference (DF&TT 2012): 16-17 May 2012,London, UK (pp. 1-6). IEEE conference proceedings, 2012(595 CP)
Open this publication in new window or tab >>Gaussian mixture PHD filter for multi-target tracking using passive doppler-only measurements
2012 (English)In: IET Conference Publications: vol 2012, issue 595 CP, IEEE conference proceedings, 2012, Vol. 2012, no 595 CP, p. 1-6Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we analyze the performance of the Gaussian mixture probability hypothesis density (GM-PHD) filter in tracking multiple non-cooperative targets using a passive sensor network. Non-cooperative transmissions from illuminators of opportunity like GSM base stations, FM radio transmitters or digital broadcasters are exploited by non-directional separately located Doppler measuring sensors. Clutter, missed detections and multi-static Doppler variances are incorporated into a realistic multi-target scenario. Simulation results show that the GM-PHD filter successfully tracks multiple targets using only Doppler shift measurements in a passive multi-static scenario.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2012
Series
IET Conference Publications
Keywords
Doppler measurements; Gaussian mixture probability hypothesis density filter; Multi-target tracking; Random sets
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-101132 (URN)10.1049/cp.2012.0417 (DOI)E-ISBN: 978-1-84919-624-6 (ISBN)
Conference
Data Fusion & Target Tracking Conference (DF&TT 2012): 16-17 May 2012,London, UK
Available from: 2013-11-20 Created: 2013-11-19 Last updated: 2013-11-20
Gustafsson, F., Orguner, U., Schön, T. B., Skoglar, P. & Karlsson, G. R. (2012). Navigation and Tracking of Road-Bound Vehicles. In: Eskandarian, Azim (Ed.), Handbook of Intelligent Vehicles: (pp. 397-434). London: Springer
Open this publication in new window or tab >>Navigation and Tracking of Road-Bound Vehicles
Show others...
2012 (English)In: Handbook of Intelligent Vehicles / [ed] Eskandarian, Azim, London: Springer, 2012, p. 397-434Chapter in book (Refereed)
Abstract [en]

The Handbook of Intelligent Vehicles provides a complete coverage of the fundamentals, new technologies, and sub-areas essential to the development of intelligent vehicles; it also includes advances made to date, challenges, and future trends. Significant strides in the field have been made to date; however, so far there has been no single book or volume which captures these advances in a comprehensive format, addressing all essential components and subspecialties of intelligent vehicles, as this book does. Since the intended users are engineering practitioners, as well as researchers and graduate students, the book chapters do not only cover fundamentals, methods, and algorithms but also include how software/hardware are implemented, and demonstrate the advances along with their present challenges. Research at both component and systems levels are required to advance the functionality of intelligent vehicles. This volume covers both of these aspects in addition to the fundamentals listed above. 

Place, publisher, year, edition, pages
London: Springer, 2012
Keywords
Engineering, Artificial intelligence, Automotive Engineering, Control, Robotics, Mechatronics
National Category
Signal Processing Control Engineering
Identifiers
urn:nbn:se:liu:diva-74661 (URN)978-0-85729-084-7 (ISBN)
Available from: 2012-02-03 Created: 2012-02-03 Last updated: 2014-11-20Bibliographically approved
Saha, S., Orguner, U. & Gustafsson, F. (2012). Nonlinear Filtering based on Observations from Student's T Processes. In: Proceedings of the 2012 IEEE Aerospace Conference: . Paper presented at 2012 IEEE Aerospace Conference, Big Sky, MT, USA, 3-10 March, 2012.
Open this publication in new window or tab >>Nonlinear Filtering based on Observations from Student's T Processes
2012 (English)In: Proceedings of the 2012 IEEE Aerospace Conference, 2012, , p. 6Conference paper, Published paper (Refereed)
Abstract [en]

We consider measurements from possibly zero-mean stochastic processes in a nonlinear filtering framework. This is a challenging problem, since it is only the second order properties of the measurements that bear information about the unknown state vector. The covariance function of the measurements can have both spatial and temporal correlation that depend on the state. Recently, a solution to this problem was presented for the case of Gaussian processes. We here extend the theory to Student's t processes. We illustrate the state observability by a simple but still realistic simulation example.

Publisher
p. 6
Keywords
Nonlinear filtering, Particle filtering, Student's t process
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-79604 (URN)10.1109/AERO.2012.6187210 (DOI)978-1-4577-0556-4 (ISBN)
Conference
2012 IEEE Aerospace Conference, Big Sky, MT, USA, 3-10 March, 2012
Funder
Swedish e‐Science Research Center
Note

Funder: Linnaeus research environment CADICS, funded by the Swedish Research Councilfor the financial support.

Available from: 2012-09-27 Created: 2012-08-10 Last updated: 2018-07-03Bibliographically approved
Ardeshiri, T., Orguner, U., Lundquist, C. & Schön, T. (2012). On mixture reduction for multiple target tracking. In: : . Paper presented at Information Fusion (FUSION), 2012 15th International Conference on.
Open this publication in new window or tab >>On mixture reduction for multiple target tracking
2012 (English)Conference paper, Published paper (Refereed)
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-100233 (URN)
Conference
Information Fusion (FUSION), 2012 15th International Conference on
Available from: 2013-10-31 Created: 2013-10-31 Last updated: 2013-12-19Bibliographically approved
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