Improved GM-PHD Filter Based on Multi-target Uncertainty

WANG Kuiwu;ZHANG Qin;HU Xiaolong

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  • Sponsored by: China Association for Science and Technology (CAST)

    Editor-In-Chief: Xu Yida

    ISSN 1000-1093

    　

  • Hosted By: China Ordnance Society

    Published By: Acta Armamentarii

    CN 11-2176/TJ

Acta Armamentarii, a Chinese monthly journal, was first published in 1979. The journal is supervised by China Association for Science and Technology, sponsored by China Ordnance Society，and edited and published by Editorial Board of Acta Armamentarii. The Journal is included in all important domestic databases，and also collected by Engineering Index (EI, USA), Chemical Abstracts (CA, USA), Science Abstracts (SA, UK）, Abstract Journal (AJ, Russia), and Current Bibliography on Science and Technology(CBST).Acta Armamentarii has been selected into the journal Of Ordnance Science and Technology (Weapons Industry) Field Of High Quality Scientific and Technological Periodical Classification Catalogue (2022 edition), ranking T1, and the journal of Automotive Engineering Field of High Quality Scientific and technological Periodical classification Catalogue, ranking T2.
Acta Armamentarii ›› 2022, Vol. 43 ›› Issue (12) : 3113-3121. DOI: 10.12382/bgxb.2021.0693
Paper

Improved GM-PHD Filter Based on Multi-target Uncertainty

  • WANG Kuiwu1,2, ZHANG Qin1, HU Xiaolong1
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Abstract

Gaussian mixture probability hypothesis density (GM-PHD) filtering based on random finite set (RFS) is an effective method for solving multi-target tracking (MTT) problem. However, the GM-PHD filter in a dense clutter environment may have a poorer tracking performance due to excessive estimation errors. This is mainly because the uncertainty from the multi-target measurement is not fully considered. Every moment, the GM-PHD filter generates a new Gaussian component and a specific measured value. When clutter density is high or the detection probability is low, the accuracy of the estimated value of the target is lower. To solve this problem, this paper proposes a change in the covariance update formula through adjusting the component value with the weight of the Gaussian component considered, and introduces a label to merge the Gaussian components with an adaptive threshold, so as to improve the accuracy of the target number estimation. The proposed algorithm is proven to have a significantly higher target number estimation accuracy and filtering performance than traditional algorithms through tests under tracking scenarios with different clutter and detection probability conditions.

Key words

multi-targettracking / RFS / GM-PHDfilter / gaussianmixture / stateestimation

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WANG Kuiwu, ZHANG Qin, HU Xiaolong. Improved GM-PHD Filter Based on Multi-target Uncertainty. Acta Armamentarii. 2022, 43(12): 3113-3121 https://doi.org/10.12382/bgxb.2021.0693

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