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针对无迹卡尔曼滤波(UKF)算法用于实时弹道测量数据处理中存在的非线性估计精度不高、实时性不好和滤波不稳定等问题,提出了一种自适应UKF算法.该算法借鉴强跟踪滤波思想,通过渐消因子在线修正调整状态预测协方差矩阵,实时调整增益矩阵,补偿弹道模型偏差.算法采用无偏转换测量处理量测方程,在保证滤波精度的同时,简化了滤波算法.仿真结果表明:该算法的滤波精度、收敛速度、稳定性和实时性均优于标准UKF算法,能有效用于实时弹道测量数据处理.
The Unscented Kalman Filter (UKF) is an adaptive UKF algorithm which is used to solve the problems of low accuracy, poor real-time performance and unsteady filtering in real-time ballistic data processing. The strong tracking filter idea is adopted, the covariance matrix is adjusted online by means of fading factors, and the gain matrix is adjusted in real time to compensate for the deviation of the trajectory model. The algorithm uses the unbiased conversion measurement processing measurement equation to ensure the filtering accuracy and simplify the filtering algorithm The simulation results show that the proposed algorithm is superior to the standard UKF algorithm in filtering accuracy, convergence speed, stability and real-time performance, and can be effectively used in real-time ballistic data processing.