Abstract:

In real radar target tracking system, the measure data of radar are often distributed by the glint noise. The performances of conventional filters degrade severely in the presence of glint noise. An improved particle filter (PF) is proposed. The iterated extended Kalman filter (IEKF) is modified by providing a new measurement update based on Gauss-Newton iteration, and then the modified IEKF is used to generate the proposal distribution. Additionally, the Markov chain Monte Carlo (MCMC) step is adopted to counteract the problem of particle impoverishment caused by resampling step, and the diversity of the particles is maintained. Based on the glint noise statistical model, the proposed method is compared with the PF and the MCMCPF via simulations. It is shown that the proposed method outperforms both the PF and the MCMCPF.