A Fuzzy Adaptive Strong Tracking Algorithm with Fading Factor

Abstract

The maximum acceleration parameter determines the effect of the current statistical (CS) model. Thus, when tracking weak maneuvering targets or targets whose actual acceleration exceeds the given value, the tracking performance of the traditional algorithm that sets with a priori fixed value will drop sharply. To solve this problem, a fuzzy adaptive strong tracking algorithm with fading factor (IAFCS-IMM) is proposed. The algorithm adopts a two-level fuzzy logic system. Through the first-level fuzzy logic, a maneuvering factor representing the maneuverability of the target is obtained according to the estimated acceleration information of the model, and the maximum acceleration parameter is adaptively modified. The second-level fuzzy logic is adopted to adjust the model update probability of interacting multiple model (IMM) algorithm according to the maneuver factor. Besides, a fading factor is introduced in the filtering process, which can enhance the robustness of the filter to the sharp mutation of the target state. Simulation results demonstrate that IAFCS-IMM algorithm achieves good results in filtering accuracy and tracking stability of maneuvering targets.