Multi-target GM-PHD trackers based on strong tracking cubature Kalman filter

Multi-target tracking technology based on probability hypothesis density (PHD) filter has become a hot research topic due to the feature that it does not require measurement-to-track association. Aiming at the problem that the Gaussian mixture probability hypothesis density (GM-PHD) cannot update the mean and covariance in a nonlinear system, a cubature integration method is used to numerically compute multivariate moment integrals and a suboptimal fading factor of strong tracking filter is introduced to enhance the filter performance in this paper. The proposed algorithm is referred as STCKF-GM-PHD, which combines strong tracking cubature Kalman filter with GM-PHD and realizes the application of GM-PHD in a nonlinear situation. Simulation results support that the proposed approach STCKF-GM-PHD has obvious performance improvement over EKF-GM-PHD and UKF-GM-PHD in numerical stability and filtering accuracy.