RCS Information Aided Poisson Multi-Bernoulli Mixture Filter in Clutter Background

For modern radar systems, the measurement of the target’s radar cross section (RCS) is a standard output besides kinematic measurements. It is straightforward to incorporate RCS information into tracking algorithms for performance improvements in more realistic and difficult scenarios. However, in practice, the proper integration of RCS recursion and Bayesian filter is promising but challenging. To address this issue, an RCS information aided Poisson multi-Bernoulli mixture (RCSI-PMBM) filter in clutter background is proposed in this article. First, the Bayesian RCS estimation strategy is presented, and then, the RCSI-PMBM filter is analytically developed. Moreover, based on the Gamma Gaussian mixture (GGM) form, an effective and efficient implementation of the proposed RCSI-PMBM filter is developed. Finally, the validity of the proposed algorithm is verified by simulation tests with challenging scenarios.