Multi-Ellipsoidal Extended Target Tracking Using Sequential Monte Carlo

Date

2018-07-10

Author

Kara, Süleyman Fatih
Özkan, Emre

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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In this paper, we consider the problem of extended target tracking, where the target extent cannot be represented by a single ellipse accurately. We model the target extent with multiple ellipses and solve the resulting inference problem, which involves data association between the measurements and sub-objects. We cast the inference problem into sequential Monte Carlo (SMC) framework and propose a simplified approach for the solution. Furthermore, we make use of the Rao-Blackwellization, aka marginalization, idea and derive an efficient filter to approximate the joint posterior density of the target kinematic states and target extent. Conditional analytical expressions, which are essential for Rao-Blackwellization, are not available in our problem. We use variational Bayes technique to approximate the conditional densities and enable Rao-Blackwellization. The performance of the method is demonstrated through simulations. A comparison with a recent method in the literature is performed.

Subject Keywords

Extended target tracking, Random matrix, Marginalized particle filter, Rao-Blackwellization, Variational Bayes, Inverse Wishart

URI

https://hdl.handle.net/11511/39920

DOI

https://doi.org/10.23919/icif.2018.8455436

Conference Name

21st International Conference on Information Fusion (FUSION)

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

S. F. Kara and E. Özkan, “Multi-Ellipsoidal Extended Target Tracking Using Sequential Monte Carlo,” presented at the 21st International Conference on Information Fusion (FUSION), Cambridge, ENGLAND, 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/39920.