基于扩展集员滤波与信息几何的机动目标跟踪

面对当前日益复杂的雷达跟踪目标和环境，广泛应用的卡尔曼滤波及其扩展类算法虽然能以较少的计算量得到较好的跟踪效果，但其要求过程噪声与量测噪声符合零均值高斯分布，否则会导致滤波发散。为解决系统状态及噪声未知但有界情况下的机动目标跟踪问题，介绍了一种扩展集员滤波机动目标跟踪算法，并辅以基于信息几何的跟踪性能监测处理。通过仿真分析验证了基于集员滤波与信息几何的机动目标跟踪边界约束性能及跟踪准确度较好，且能通过统计流形距离的变化来探测目标是否发生机动。

Maneuvering target tracking with the extended set-membership filter and information geometry

With the increasingly complex tracking targets and environment in radar application, the widely used Kalman filter and extended versions can track targets with less few calculations. However, it requires the system noises including process noise and measurement noise in a zero mean and Gaussian case, otherwise, it may lead to poor performance. In order to solve the problem of tracking a maneuvering target, when process and measurement noises are unknown-but-bounded, an extended set-membership filter and the tracking quality method based on information geometry are proposed. Simulation analysis shows that the set-membership filter provides distinct advantages in tracking performance including state bounding and tracking accuracy, and the maneuver behaviors of target can be identified through counting the changes of distances on the manifold.