基于新息自适应的扩展卡尔曼滤波雷达目标跟踪算法

针对复杂环境下雷达目标跟踪系统易受外界干扰引入噪声污染分布问题，为了保证系统实时可靠，提出了一种基于新息自适应的扩展卡尔曼滤波雷达目标跟踪算法(innovation-based adaptive extended Kalman filter, IAEKF)。通过建立系统新息统计特性，构造系统与量测噪声函数，将新息协方差直接引入滤波器增益矩阵计算，在不增加计算代价的同时，改善算法的自适应性。仿真实验表明，在雷达测量系统受时变噪声污染分布影响下，IAEKF算法相比EKF算法跟踪精度高，算法可行且有效，具有一定的工程研究价值。

An Adaptive Extended Kalman Filter algorithm based on Innovation Sequence for Radar target tracking

In a radar target tracking system in a complex environment, the noise pollution distribution problem is caused by external interference. In order to ensure the reliable implementation of the system, an innovation-based adaptive EKF radar target tracking algorithm (IAEKF) is proposed. By establishing the statistical characteristics of system innovation covariance is directly introduced into the filter gain matrix calculation. The algorithm improves the adaptability of the algorithm without increasing the computational cost. Simulation test show that the IAEKF algorithm has higher tracking accuracy than the EKF algorithm in the case of radar measurement noise pollution distribution. The algorithm is feasible and effective, and has certain engineering research value.