Joint state estimation and mode identification based on MCMC-Gibbs sampling for OTHR

Target tracking of over the horizon radar (OTHR) faces the challenge of the low detection probability, low sampling rate, low measurement accuracy and the multipath propagation. Both mode recognition of multipath propagation and state estimation significantly affect the tracking performance. In this paper, the method of joint state estimation and mode identification based on Markov Chain Monte Carlo-Gibbs (MCMC-Gibbs) sampling for OTHR target tracking is proposed. Validation gates are firstly constructed for every mode to generate only those hypotheses that satisfy the validation gate requirement to eliminate the number of hypotheses significantly. Then the association events are obtained through MCMC-Gibbs sampling to further calculate the decision cost. Next, multiple simultaneous measurement filters are proposed to update the conditional state estimation and covariance for estimation cost. Finally, Bayes risk for joint decision and estimation is introduced to find the optimal solution. Simulation results show the effectiveness of the proposed method compared with the multipath data association tracker (MPDA) method at some sacrifice to computation cost.