Improved Algorithm for Importance Density Sample in Particle Filter ?

Particle filtering is a technique used for filtering non-linear and non-Gaussian dynamical systems. It has found widespread applications in detection, navigation, and tracking problems. Although, in general, particle filtering methods yield improved results, it is difficult to achieve real time performance. This paper presents an improved algorithm to get better convergence. This algorithm uses the Kalman filter to construct importance density function of the particle filter for state prediction. This improved algorithm fully considers the problem that there is limited priori information at the beginning of filter in maneuvering target tracking. It is shown that this algorithm could gain better tracking results through simulation results.