针对密集目标场景下的多目标跟踪算法易出现航迹混批的问题，考虑引入RCS信息辅助跟踪，提出了一种基于Gamma分布的贝叶斯RCS估计的多目标跟踪算法。首先，提出目标RCS状态及量测滤波过程，使用非平稳自回归Gamma过程对状态动力学进行建模，在时间更新中实现贝叶斯RCS估计。然后，在PHD滤波器中引入贝叶斯RCS估计，提出了PHDwRCS滤波器，实现对密集目标的跟踪。针对PHD类滤波器无法实时形成航迹、跟踪精度较低的问题，在TPHD滤波器中引入RCS估计，提出了TPHDwRCS滤波器，实现了对密集目标的有效航迹跟踪。通过计算机仿真实验表明，所提算法能够有效实现贝叶斯RCS估计，引入RCS信息后的PHDwRCS滤波器和TPHDwRCS滤波器能够实现对密集目标的精确跟踪，基于GOSPA度量的定量误差性能得到提升，一定程度上缓解了航迹混批问题。

To address the issue of track mixing in multi-target tracking algorithms under dense target scenarios, this paper proposes a multi-target tracking algorithm based on Bayesian radar cross section (RCS) estimation using the Gamma distribution, which incorporates RCS information to assist in tracking. Firstly, the target RCS state and measurement filtering process are presented. A non-stationary autoregressive Gamma process is used to model the state dynamics, enabling Bayesian RCS estimation during the time update. Then, Bayesian RCS estimation is introduced into the probability hypothesis density (PHD) filter, resulting in the PHDwRCS filter, which enables tracking of dense targets. To address the limitations of PHD-based filters in real-time track formation and low tracking accuracy, RCS estimation is further integrated into the Track-before-Detect (TPHD) filter, yielding the TPHDwRCS filter, which achieves effective track tracking of dense targets. Computer simulation experiments demonstrate that the proposed algorithm can effectively implement Bayesian RCS estimation. The PHDwRCS and TPHDwRCS filters incorporating RCS information can accurately track dense targets, result- ing in improved quantitative error performance based on the generalized optimal subpattern assignment (GOSPA) metric. This approach mitigates the problem of track mixing to a certain extent.


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