An improved adaptive tracking algorithm for group targets

Abstract:

In order to improve tracking performance of the approach, a new adaptive tracking algorithm of group maneuvering targets was presented. In the estimation of group centroid kinematic state, the deviation between the prediction value and estimation value of centroid speed was used to adjust the covariance matrix of process noise based on modified current statistical model, and a fading factor of strong tracking filter was used to adjust the state-estimation error covariance adaptively. In the estimation of group extension state, the prediction parameter of extension was calculated by using a fuzzy reasoning method, which had taken the deviation between the prediction value and estimation value of the corresponding elliptical area and the change ratio of deviation as the input of the fuzzy controller. Lastly, a method to judge split-off maneuvering of group targets was offered. Simulation results show that, compared with the existing methods, the proposed algorithm can obtain a better adaptive tracking performance in maneuvering scenarios, and detect the split-off maneuvering effectively.