Data association and passive tracking for multiple targets based on Gaussian sum particle filter

Abstract

To improve tracking performance of multi-target tracking, a data association and passive tracking scheme based on the joint probabilistic data association (JPDA) and the Gaussian sum particle filter (GSPF) is proposed. The GSPF is presented to formulate the problem of passive tracking. Compared with sampling importance resampling (SIR) scheme, GSPF can incorporate the most current observations into the particle filter and generate accurate proposal density distribution for the particle filter. Hence, the proposal scheme uses GSPF to track the states of the targets and applies the idea of JPDA directly to the sample sets of multi-target states, and weights of particles are evaluated through the combination of JPDA. The specific implementation steps of JPDA based on GSPF (JPDA/GSPF) are deduced. Trajectory tracking and the root mean square error (RMSE) comparisons are made with JPDA/SIR and JPDA/EKF schemes on simulated data in multi-target passive tracking. Simulation results show that the JPDA/GSPF scheme has better performance than JPDA/SIR and JPDA/EKF scheme in tracking. Furthermore, from the view of particle cost, the JPDA/GSPF introduces higher computation efficiency than JPDA/SIR.