Abstract:

For the low observable problems of multiple target passive tracking in nonlinear and non-Gaussian environment, combining with particle filter (PF), joint probabilistic data association (JPDA) and amplitude information of measurements, a joint probabilistic data association particle filter algorithm based on amplitude information is proposed. In this algorithm, the association likelihood of JPDA is combined with the likelihood ratio of amplitude, the particle filter algorithm is used to track targets, and the amplitude information of measurements is used to improve target tracking performance under the condition of low observability. Simulation results demonstrate that the proposed algorithm has an improved data association reliability and target tracking accuracy.