Variational Bayesian adaptation of noise covariances in trajectory PHD filter

Zhixian Han; Biying Jiang; Xingchen Lu

doi:10.1117/12.2671196

29 March 2023 Variational Bayesian adaptation of noise covariances in trajectory PHD filter

Zhixian Han, Biying Jiang, Xingchen Lu

Proceedings Volume 12594, Second International Conference on Electronic Information Engineering and Computer Communication (EIECC 2022); 1259425 (2023) https://doi.org/10.1117/12.2671196
Event: Second International Conference on Electronic Information Engineering and Computer Communication (EIECC 2022), 2022, Xi'an, China

Abstract

The trajectory probability hypothesis density (TPHD) filter is a promising multi-target tracking algorithm. However, in many practical applications, inaccurate measurement noise prior statistics can degrade the performance of TPHD filters. In this paper, we model the trajectory state in TPHD as the product of the Gaussian mixture with the inverse Wishart under the linear Gaussian model, and propose the IW-VB-TPHD filter to adaptively acquire the covariance of measurement noise. Compared with the IG-VB-TPHD filter, simulation results show that the IW-VB-TPHD filter has a better accuracy in target tracking.

Citation Download Citation

Zhixian Han, Biying Jiang, and Xingchen Lu "Variational Bayesian adaptation of noise covariances in trajectory PHD filter", Proc. SPIE 12594, Second International Conference on Electronic Information Engineering and Computer Communication (EIECC 2022), 1259425 (29 March 2023); https://doi.org/10.1117/12.2671196

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