An improved UKF (Unscented Kalman Filter) algorithm is proposed to solve the problem of radar azimuth mutation. Since the radar azimuth angle will restart to count after each revolution of the radar, and when the aircraft just passes the abrupt angle change, the radar observation measurement will have a sudden change, which has serious consequences and is solved by the proposed novel UKF based on SVD. In order to improve the tracking accuracy and stability of the radar tracking system further, the SVD-MUKF (Singular Value Decomposition-based Memory Unscented Kalman Filter) based on multiple memory fading is constructed. Furthermore, several simulation results show that the SVD-MUKF algorithm proposed in this paper is better than the SVD-UKF (Singular Value Decomposition of Unscented Kalman Filter) algorithm and classical UKF algorithm in accuracy and stability. Last but not the least, the SVD-MUKF can achieve stable tracking of targets even in the case of angle mutation.

中文翻译：

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雷达方位角突变的改进的无味卡尔曼滤波算法

为了解决雷达方位角突变问题，提出了一种改进的UKF（Unscented Kalman Filter）算法。由于雷达方位角将在雷达每旋转一圈后重新开始计数，并且当飞机刚刚通过突然的角度变化时，雷达观测测量值将发生突然变化，这将产生严重的后果，并通过提出的新型UKF解决在SVD上。为了进一步提高雷达跟踪系统的跟踪精度和稳定性，构造了基于多存储衰落的SVD-MUKF（基于奇异值分解的存储无味卡尔曼滤波器）。此外，若干仿真结果表明，本文提出的SVD-MUKF算法在准确性和稳定性方面优于SVD-UKF（无味卡尔曼滤波器的奇异值分解）算法和经典UKF算法。最后但并非最不重要的一点是，即使在角度突变的情况下，SVD-MUKF也可以实现对目标的稳定跟踪。