In this paper, a novel maximum fuzzy correntropy Kalman filter (MFC-KF) algorithm is proposed to solve the problem that the effect of different samples on state estimation is uncertain in common correntropy. In the proposed algorithm, a new optimization criterion—the maximum fuzzy correntropy criterion with fuzzy correntropy based on fuzzy information theory—is used to optimize the Kalman filter, by reducing the effect of the common correntropy applying the same weight for all samples. Moreover, to apply the MFC-KF algorithm to bearings-only maneuvering target tracking, it is combined with the least-squares method for measurement conversion. Moreover, the kernel width is set adaptively. Simulations show that the proposed algorithm can track a target more accurately than the interactive multi-model extended Kalman filter (IMMEKF), the interactive multi-model unscented Kalman filter (IMMUKF), or the maximum correntropy Kalman filter (MCKF).

提出了一种新颖的最大模糊熵卡尔曼滤波算法（MFC-KF），以解决在普通的熵中不确定样本对状态估计的影响。在提出的算法中，使用新的优化准则-基于模糊信息论的具有模糊熵的最大模糊熵准则-通过减少对所有样本施加相同权重的共同熵的影响，来优化Kalman滤波器。此外，为了将MFC-KF算法应用于纯方位机动目标跟踪，它与最小二乘法相结合进行测量转换。此外，内核宽度是自适应设置的。仿真结果表明，与交互式多模型扩展卡尔曼滤波器（IMMEKF）相比，该算法能够更准确地跟踪目标，