Abstract: 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.

Key words: multi-target tracking, RCS, Bayesian estimation, random finite set, PHD filtering, TPHD filtering