Time-varying PHD filter for multi-target tracking without the detection probability

Xinhui Wu; Changfei Wang; Yu Zhou

doi:10.1117/12.2640254

23 May 2022 Time-varying PHD filter for multi-target tracking without the detection probability

Xinhui Wu, Changfei Wang, Yu Zhou

Proceedings Volume 12254, International Conference on Electronic Information Technology (EIT 2022); 122540I (2022) https://doi.org/10.1117/12.2640254
Event: International Conference on Electronic Information Technology (EIT 2022), 2022, Chengdu, China

Abstract

In Bayesian multi-target filtering, knowledge of detection probability is of critical importance. Significant mismatches in detection probability parameter result in biased estimates. In this paper, we propose a time-varying probability hypothesis density (PHD) filter to deal with unknown detection probability. The detection probability for each target is assured when the predicted states associate with the measurements, and closed-form solutions to the PHD filter are derived by some modifications of the measurement updated formula. Unlike other methods for this challenging class of the PHD filters, the novel method can deal with the unknown and time-varying detection probability effectively, without the complicated process of parameter estimation. Simulations show that the proposed algorithm is suitable to solve the unknown detection probability problems and have better performance, implying good application prospects.

Citation Download Citation

Xinhui Wu, Changfei Wang, and Yu Zhou "Time-varying PHD filter for multi-target tracking without the detection probability", Proc. SPIE 12254, International Conference on Electronic Information Technology (EIT 2022), 122540I (23 May 2022); https://doi.org/10.1117/12.2640254

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