Based on Iterated Extend Kalman Particle Filter

A technique for fusing data from radar/infrared Multi-sensor was developed to track maneuvering target. Modified Iterated Extend Kalman Particle Filter is simple yet very effective in accounting for the measurement nonlinearities. The idea of fusion is to combine IEK-PF with pseudo sequential filter to obtain optimum state estimates. The main idea uses the system state transition matrix and the error covariance matrix which are gained from the IEKF and the sequential fusion to construct the importance density function of the particle filter. So the importance density function can integrate the latest observation into system state transition density, and the proposal distribution can approximate the posterior distribution reasonably well. The simulation results show that this technique can overcome the flaw that it is hard to get the optimization importance density function in the particle filter and significantly improve the accuracy of state estimation.