为解决基于“当前”统计模型的自适应滤波器对弱机动,特别是非机动目标跟踪精度下降问题,提出基于模糊神经网络的目标自适应跟踪算法,对并行工作的两滤波器进行数据融合.仿真结果表明:与一般自适应算法相比,该算法对各种机动程度的目标跟踪精度均有不同程度的提高,能更好地适应目标的各种运动形式,尤其适用于对目标的速度和加速度估计精度要求较高的场合,在指控、火控系统中具有实用价值. In order to solve the problem that the adaptive filter based on the “current” statistical model reduces the tracking accuracy of weak maneuvers, especially non-maneuvering targets, a target adaptive tracking algorithm based on fuzzy neural network is proposed to fuse the two filters working in parallel. The results show that compared with the general adaptive algorithm, the proposed algorithm can improve the target tracking accuracy of various maneuvering degrees to a great extent, and can adapt to all kinds of motion of the target well, especially for the target’s speed and acceleration High accuracy of the estimated occasions, the allegations, fire control system has practical value.