改进的CS-UKF加速度方差自适应跟踪算法

Modified Acceleration Variance Adaptive Tracking Algorithm Of CS_UKF

摘要:

针对基于当前统计模型的状态噪声协方差阵中的加速度方差调整方法对一般机动目标、非机动目标跟踪精度差的问题,研究其改进方法；在建立机动目标当前统计模型离散状态方程和雷达导引头离散观测方程的基础上；利用雷达导引头测量信息和位置预测值之间的扰动对加速度方差进行调整,提出了改进的加速度方差自适应调整无迹卡尔曼滤波跟踪算法；数字仿真验证了该算法对非机动目标、一般机动目标以及高机动目标均具有良好的跟踪效果。

Abstract:

Acceleration variance adaptive adjustment method of maneuvering target current statistical model has low tracking precision for weak maneuvering target and non maneuvering target. The paper puts forward a modified method. Discrete state equation of maneuvering target current statistical model is founded. Discrete observation equation of radar seeker is also founded. This paper puts forward an improved acceleration variance adaptive adjustment algorithm of unscented kalman filtering(UKF),which uses disturbance between radar seeker observational information and prediction value of position to self-adaptive adjustment acceleration variance.The simulation shows that modified CS_UKF algorithm has better tracking precision for weak maneuvering target, non maneuvering target and high maneuvering target.