Maneuvering target tracking with non-Gaussian noise

Tracking a maneuvering target in a nonlinear interference environment has been discussed in some literature, and this paper considers the problems of tracking in the non-Gaussian noise environment and then tracking with multiple passive sensors. The method of target state estimation in this paper is a dynamic programming approach. Unlike the traditional method, the estimating problem is reduced to a multiple hypothesis-testing problem, and then is solved by using the dynamic programming algorithm. The simulation results show the superiority of the new method to EKF.