Multi-Sensor Control for Jointly Searching and Tracking Multi-Target Using the Poisson Multi-Bernoulli Mixture Filter

Ke Chen, Lei Chai, Wei Yi
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Abstract

In this paper, a multi-sensor control method with limited sensor field-of-view (FoV) based on Poisson multi-Bernoulli mixture (PMBM) filter is proposed, which provides a solution to the multi-target joint search and tracking problem. First, we propose a robust fusion method for multi-sensor PMBM posterior densities with limited sensor FoV based on the generalized covariance intersection (GCI) fusion criterion. Then, a cost function is constructed for multi-sensor control based on the fused density, which takes into account both discovered and undiscovered targets. The optimal multi-sensor control scheme is obtained by minimizing the cost function. The effectiveness of the proposed control method is demonstrated through a multi-target joint search and tracking simulation with unknown and time-varying number of targets and limited FoVs of sensors.
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基于泊松-伯努利混合滤波的多传感器联合搜索与跟踪控制
提出了一种基于泊松-伯努利混合滤波的有限传感器视场多传感器控制方法,解决了多目标联合搜索与跟踪问题。首先,提出了一种基于广义协方差交集(GCI)融合准则的有限视场下多传感器PMBM后验密度鲁棒融合方法。然后,在融合密度的基础上,构造了同时考虑发现目标和未发现目标的多传感器控制代价函数;通过最小化代价函数,得到了最优的多传感器控制方案。通过目标数量未知时变、传感器视场受限的多目标联合搜索跟踪仿真,验证了所提出控制方法的有效性。
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