Interactive Model Fusion-Based GM-PHD Filter

Autor:	He, Jiacheng, Zhong, Shan, Peng, Bei, Wang, Gang, Wang, Qizhen
Rok vydání:	2023
Předmět:	Electrical Engineering and Systems Science - Systems and Control
Druh dokumentu:	Working Paper
Popis:	In multi-target tracking (MTT), non-Gaussian measurement noise from sensors can diminish the performance of the Gaussian-assumed Gaussian mixture probability hypothesis density (GM-PHD) filter. In this paper, an approach that transforms the MTT problem under non-Gaussian conditions into an MTT problem under Gaussian conditions is developed. Specifically, measurement noise with a non-Gaussian distribution is modeled as a weighted sum of different Gaussian distributions. Subsequently, the GM-PHD filter is applied to compute the multi-target states under these distinct Gaussian distributions. Finally, an interactive multi-model framework is employed to fuse the diverse multi-target state information into a unified synthesis. The effectiveness of the proposed approach is validated through the simulation results. Comment: conference
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2309.08088 Zobrazit plný text záznamu View this record from Arxiv