An Iterated Extend Kalman Particle Filter for Multi-sensor based on pseudo sequential fusion

In order to overcome the flaw that it is hard to get the optimization importance density function in the particle filter. The IEKF and the sequential fusion were integrated with particle filter. Than, the particle filter was introduced to radar/infrared Multi-sensor target fusion tracking. The main idea is use 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 integrates the latest observation into system state transition density, and the proposal distribution can approximates the posterior distribution reasonably well. The simulation results show that the iterated extend Kalman particle filter based on sequential fusion can significantly improve the accuracy of state estimation.