Abstract:

The current statistical model and adaptive Kalman filter algorithm have a good performance on strong maneuvering targets tracking, but poor on weak and non-motorized maneuvering targets. To solve this problem, a bell shape function is utilized as fuzzy membership function to adjust the upper and lower limits of target acceleration. Then the algorithm can adjust the process noise variance of stable acceleration adaptively and improves the tracking accuracy effectively. By using the idea of fading factor of the strong tracking filter, a fading factor is proposed to adjust revised extreme value of acceleration. The delay time of tracking can be shortened obviously when there is a sudden maneuver or the acceleration changed greatly. Simulation results show that the algorithm has a good performance on tracking weak and non-maneuvering maneuvering targets.