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当前统计模型及其自适应卡尔曼滤波算法对强机动目标具有很好的跟踪效果,但当机动目标为弱机动和非机动时算法跟踪性能较差。针对这一问题,提出了采用铃形函数作为模糊隶属函数对模型中加速度极值进行修正的自适应滤波算法,调整加速度稳定时的系统过程噪声方差,提高算法的跟踪精度。同时,借鉴强跟踪滤波算法的渐消自适应滤波因子思想,针对加速度突变的情况引入渐消因子对修正的加速度极值进行调节,提高算法在加速度突变情况下的跟踪速度。仿真实验结果表明,算法对弱机动目标和非机动目标的跟踪具有良好的效果。
The current statistical model and its adaptive Kalman filter algorithm have a good tracking effect on strong maneuvering targets, but the tracking performance is poor when the maneuvering targets are weak and non-maneuvering. In order to solve this problem, an adaptive filtering algorithm is proposed to modify the acceleration extremum in the model by using the bell-shaped function as the fuzzy membership function. The variance of the system process noise during the acceleration stabilization is adjusted and the tracking accuracy of the algorithm is improved. At the same time, referring to the idea of fading tracking adaptive filter, aiming at the abrupt change of acceleration, the gradual elimination factor is introduced to adjust the modified acceleration extremum and the tracking speed of the algorithm under the condition of sudden acceleration is improved. Simulation results show that the algorithm has good effect on the tracking of weak maneuvering targets and non-maneuvering targets.