Nonlinear Target Tracking Algorithm Based on Particle Filters

Particle Filter is presented to solve the nonlinear filter and non-Gaussian problem,while the algorithms of Kalman Filter and Extended Kalman Filter within the Gaussian background leads to the filter precision decrease and divergence phenomenon. As a nonlinear filter algorithm based on Bayesian estimation,particle filter has original advantage at treating the parameter estimation and state filtering aspects of nonlinear non-Gaussian time-varying systems,but it takes a lot of time due to larger number of particles.Thereby Extended Kalman Particle Filter is presented to solve the lower the real-time performance resulting from high computational complexity. The simulation results show that the PF approach outperforms the EKF algorithm under strong nonlinear and non-Gaussian environment,and EKPF gives better performance than EKF in solving high computation complexity.