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In the framework of random finite set (RFS) theory, a V-shaped multi-agent formation (VMAF) tracking algorithm based on a probability hypothesis density (PHD) filter is proposed. The algorithm incorporates the control input (CI) generated by the formation model into the PHD filter to enhance its ability to predict the localization of agents, thus enabling effective tracking and control of VMAF in environments with obstacles. The algorithm, referred to as CI-based PHD, addresses the uncertainties caused by sensor detection probabilities, clutter, and noise in formation path planning. By comparing with the standard PHD algorithm, the simulation results verify the effectiveness and robustness of CI-based PHD under different detection probabilities and Poisson clutter rates.

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A V-Shaped Multi-Agent Formation Navigation Method Based on the Probability Hypothesis Density Filter

  • 会议论文

  • 发表日期：

    2025年

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  • Yusong Zhou,
  • Jin Zhao,
  • Ping Chen,
  • Zhiwen Ma

摘要

In the framework of random finite set (RFS) theory, a V-shaped multi-agent formation (VMAF) tracking algorithm based on a probability hypothesis density (PHD) filter is proposed. The algorithm incorporates the control input (CI) generated by the formation model into the PHD filter to enhance its ability to predict the localization of agents, thus enabling effective tracking and control of VMAF in environments with obstacles. The algorithm, referred to as CI-based PHD, addresses the uncertainties caused by sensor detection probabilities, clutter, and noise in formation path planning. By comparing with the standard PHD algorithm, the simulation results verify the effectiveness and robustness of CI-based PHD under different detection probabilities and Poisson clutter rates.