A Variational Measurement Update for Extended Target Tracking With Random Matrices

Date

2012-07-01

Author

Orguner, Umut

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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.

Subject Keywords

Extended target tracking, Measurement update, Random matrices, Variational bayes

URI

https://hdl.handle.net/11511/38321

Journal

IEEE TRANSACTIONS ON SIGNAL PROCESSING

DOI

https://doi.org/10.1109/tsp.2012.2192927

Collections

Department of Electrical and Electronics Engineering, Article

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A Random Matrix Measurement Update Using Taylor-Series Approximations Sarıtaş, Elif; Orguner, Umut (2018-07-13) An approximate extended target tracking (ETT) measurement update is derived for random matrix extent representation with measurement noise. The derived update uses Taylor series approximations. The performance of the proposed update methodology is illustrated on a simple ETT scenario and compared to alternative updates in the literature.
A PHD Filter for Tracking Multiple Extended Targets Using Random Matrices Granstrom, Karl; Orguner, Umut (2012-11-01) 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 present...
Random Matrix Based Extended Target Tracking with Orientation: A New Model and Inference Tuncer, Barkın; Özkan, Emre (2021-02-01) In this study, we propose a novel extended target tracking algorithm which is capable of representing the extent of dynamic objects as an ellipsoid with a time-varying orientation angle. A diagonal positive semi-definite matrix is defined to model objects' extent within the random matrix framework where the diagonal elements have inverse-Gamma priors. The resulting measurement equation is non-linear in the state variables, and it is not possible to find a closed-form analytical expression for the true poste...
A Gaussian mixture PHD filter for extended target tracking Granström, Karl; Lundquist, Christian; Orguner, Umut (null; 2010-07-29) In extended target tracking, targets potentially produce more than one measurement per time step. Multiple extended targets are therefore usually hard to track, due to the resulting complex data association. The main contribution of this paper is the implementation of a Probability Hypothesis Density ( phd) filter for tracking of multiple extended targets. A general modification of the phd filter to handle extended targets has been presented recently by Mahler, and the novelty in this work lies in the reali...
Posterior Cramér-Rao lower bounds for extended target tracking with random matrices Sarıtaş, Elif; Orguner, Umut (2016-08-04) This paper presents posterior Cramér-Rao lower bounds (PCRLB) for extended target tracking (ETT) when the extent states of the targets are represented with random matrices. PCRLB recursions are derived for kinematic and extent states taking complicated expectations involving Wishart and inverse Wishart distributions. For some analytically intractable expectations, Monte Carlo integration is used. The bounds for the semi-major and minor axes of the extent ellipsoid are obtained as well as those for the exten...

Citation Formats

U. Orguner, “A Variational Measurement Update for Extended Target Tracking With Random Matrices,” IEEE TRANSACTIONS ON SIGNAL PROCESSING, pp. 3827–3834, 2012, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/38321.