中文摘要：

针对当前统计模型常规算法跟踪机动目标的缺陷,提出了当前统计模型模糊自适应算法。该算法根据规范化的量测新息及其变化率并通过模糊推理实时选取机动频率,给出了加速度方差的新息幂函数调整方法,采用加速度估计值和预测值的偏差在线更新当前加速度均值。在此基础上,结合高斯隶属函数和强跟踪算法对其权值予以修正。当前统计模型模糊自适应算法不受机动频率人为给定和最大加速度极值设置的限制,适用于不同范围和程度的机动。利用当前统计模型模糊自适应算法对阶跃机动、圆周机动、Jerk机动3种典型机动场景进行了计算机仿真,并与当前统计模型常规跟踪算法和Jerk模型自适应算法进行了比较。仿真结果表明,该算法扩大了跟踪范围,具有较好的稳态特性和瞬态特性,其跟踪精度和收敛速度优于其他两种算法。

英文摘要：

A fuzzy adaptive algorithm is proposed for the imperfections of tracking a maneuvering target using conventional algorithm based on current statistical model. Maneuvering frequency is adjusted in real time by fuzzy reasoning according to the normalized residual and its change rate. Acceleration variance is depicted using residual power function, and the mean value of current acceleration is updated by the de- viation between the estimated and predicted values of acceleration. On this basis, the weight of proposed algorithm is revised by Gauss membership function and strong tracking algorithm. Fuzzy adaptive algorithm is not restricted by maneuvering frequency given manually and extreme value of maximum acceleration, which is suitable for the different ranges and degrees of maneuvering. The performance of the proposed algorithm is tested by tracking three typical maneuvering targets, such as step maneuvering, circular maneuvering, and Jerk maneuvering. Simulated results show the tracking range is expanded using the pro- posed algorithm compared with the conventional tracking algorithm based on current model and the adaptive algorithm based on Jerk model. The proposed algorithm has good steady-state and transient characteristics, and its tracking accuracy and convergence rate are superior to those of the other two algorithms.