Interactive Model Fusion-Based GM-PHD Filter
Autor: | He, Jiacheng, Zhong, Shan, Peng, Bei, Wang, Gang, Wang, Qizhen |
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Rok vydání: | 2023 |
Předmět: | |
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 |
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