模糊自适应强跟踪卡尔曼滤波器研究

针对卡尔曼滤波器对系统模型依赖性强、鲁棒性差和跟踪机动目标能力有限的问题,提出了一种具备模糊自适应特点的模糊强跟踪卡尔曼滤波器(fuzzystrongtrackingKalmanfilter,FSTKF)。这种方法主要利用模糊逻辑自适应控制器监测残差均值与标准差,并根据模糊规则动态调整弱化因子,从而对强跟踪滤波器中多重次优渐消因子进行自适应调整,进一步提高滤波器的估计性能。仿真结果表明,该改进滤波器跟踪机动目标的精度高于常规卡尔曼滤波器和强跟踪卡尔曼滤波器。

Study of fuzzy adaptive strong tracking Kalman filter

A new improved strong tracking Kalman filter based on fuzzy logic adaptive controller is presented. By monitoring the residual mean and standard deviation, the fuzzy logic adaptive controller (FLAC) of this method dynamically adjusts the softening factor according to fuzzy rules. For this reason, this method modifies the fading matrix adaptively and improves estimation performance. The results of simulation indicate that this new approach has a better accuracy than the traditional Kalman filter and strong tracking Kalman filter in the field of maneuvering target tracking.