Abstract:

To get the state and number estimation of the bearingsonly targets in real time, employing the frame of a cardinalized probability hypothesis density (CPHD) for the Gaussian mixture particle （GMP）, a GMPCPHD algorithm based on unscented Kalman filter (UKF) for outliers’ rejection, called UKF-GMPCPHD algorithm for outliers’ rejection, is proposed. In the new algorithm, the sophisticated proposal distributions for the particle filter are generated by the UKF for outliers’ rejection, and the prediction and update distributions for the particles are approached by quasiMonte Carlo (QMC) method, and the probability hypothesis density (PHD) and cardinalized distributions are approximated by a mixture of Gaussian particle filtering (GPF).  Finally, the comparison has been done among the UKF-GMPCPHD, GMPCPHD and UKF-GMPPHD. Simulation results show the good tracking performance of the proposed algorithm.