Variational smoothing for extended target tracking with random matrices

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2022-4-05

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Kartal, Savaş Erdem

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In this thesis, two Bayesian smoothers are proposed for random matrix based extended target tracking (ETT). The proposed smoothers are based on the variational Bayes techniques and they are derived for an extended target model without and with orientation. The random matrix models of Feldman et al. and Tuncer and Özkan are used as the extended target models without and with orientation, respectively. The performance of both smoothers is evaluated using simulation results on two different scenarios. It is seen that the variational smoothers derived for both models outperform the previous smoother recently given in the literature on the scenario with a non-maneuvering target. On the other hand, it is seen that the performance of the smoother for the model without orientation is reduced significantly below expectations on the scenario with maneuvers. Nevertheless, the smoother for the model with orientation is shown to have little performance degradation in the maneuvering scenario. Overall the results obtained in this thesis show that: -the variational approach results in better smoothers than the existing smoother in the literature, -the explicit modeling of orientation is beneficial in smoothing as well as filtering for tracking maneuvering extended targets.

Subject Keywords

Extended target tracking, Smoother, Variational Bayes, Random matrices, Target extension, Target orientation

URI

https://hdl.handle.net/11511/96808

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Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

S. E. Kartal, “Variational smoothing for extended target tracking with random matrices,” M.S. - Master of Science, Middle East Technical University, 2022.