Passive Target Tracking Algorithm Based on Improved Gaussian Mixture Particle Filter
Yunbo Kong, Xinxi Feng, Lu Chuanguo · Modern Radar · 2012
An improved Gaussian mixture particle filter algorithm was proposed for the highly non-linear passive tracking system,the limited Gaussian mixture model was used to approximate the posterior density of states,system noise and measurement noise in the algorithm,which based on the characteristics of SPKF and particle filter.Then the genetic based EM algorithm was used to obtain the reduction of model order,which overcooked the disadvantage of the standard EM algorithm that assumed the number of the mixture components is a known priori,the performance of the overall parameter estimation process depends on the given good initial settings,and the estimated parameter can be resulted from some local optimum points.The effects caused by sampling depletion were lessened.Simulation results show that the algorithm outperforms the one based on PF,the one based on EM-GMPF and the one based on GEM-GMPF in tracking accuracy,and stability.Therefore it is more suitable to the nonlinear state estimation.