摘要： 为提升平方根容积卡尔曼滤波(SRCKF)算法在单站无源跟踪中对机动目标的跟踪性能,提出一种强跟踪修正SRCKF算法。利用标准卡尔曼滤波对状态变量及误差协方差矩阵平方根进行预测,替代原有的容积点加权和的近似计算方法。使用一次状态估计值构造新的测量方程,并结合标准卡尔曼滤波进行二次滤波估计,从而提高滤波精度。借鉴强跟踪滤波器思想,将时变渐消因子引入状态预测误差协方差阵的平方根中,实时调整增益矩阵,从而使算法具有自适应跟踪目标能力,增强其应对突变机动的鲁棒性。仿真结果表明,与SRCKF算法相比,该算法在常规机动以及突变机动下都具有更高的跟踪精度。

关键词: 单站无源跟踪, 强跟踪滤波器, 平方根容积卡尔曼滤波, 状态估计, 渐消因子

Abstract: In order to improve the performance of Square Root Cubature Kalman Filtering(STSRCKF) algorithm to track maneuvering target in single observer passive tracking,a Strong Tracking Modified SRCKF(ST-MSRCKF) algorithm is presented.Target state variables and the square root of error covariance matrix are predicted by standard Kalman filtering replacing the method of approximate calculation with weighted sum of Cubature point.A new measurement equation is established with the first estimate value of state and the target state is estimated secondly by standard Kalman filtering,thus improving the filtering accuracy.Meanwhile,with reference to the Strong Tracking Filter(STF),by adjusting the gain matrix in real-time with introducing a time-varying fading factor into the square root of the error covariance matrix,the ST-MSRCKF has the capability of adaptive target tracking and its robustness to deal with sudden change maneuver is enhanced．Simulation results show that this algorithm has higher tracking accuracy than SRCKF algorithm in case of normal maneuver and sudden change maneuver.

Key words: single observer passive tracking, Strong Tracking Filter(STF), Square Root Cubature Kalman Filtering(SRCKF), state estimation, fading factor

中图分类号: 

• TN971