Rao-Blackwellised Particle Filter for Star-Convex Extended Target Tracking Models

Date

2016-07-08

Author

Özkan, Emre
Godsill, Simon J.

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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 explore the potential gains in using Sequential Monte Carlo (SMC) methods for extended target tracking (ETT) models based on Gaussian processes (GP). The existing random hypersurface based ETT models use Extended/Unscented Kalman filter for inference, which may lead to poor performance under high uncertainty. Particle filters (PFs) are known to provide robust performance in the cases where the non-linear Kalman filtering solutions fail. We design a Rao-Blackwellised particle filter (RBPF) where we exploit the conditional linear Gaussian structure of the GP parameters. We illustrate the gain in the performance with simulations.

URI

https://hdl.handle.net/11511/55669

Conference Name

19th International Conference on Information Fusion (FUSION)

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Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

E. Özkan and S. J. Godsill, “Rao-Blackwellised Particle Filter for Star-Convex Extended Target Tracking Models,” presented at the 19th International Conference on Information Fusion (FUSION), Heidelberg, GERMANY, 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/55669.