Abstract: Standard Kalman filter algorithm assumes system mathematical model and statistical noise characteristics；it easily leads to errors and even divergence when the assumptive and actual models do not match.Proposed Kalman filter adaptive algorithm based on reverse prediction，by comparing the original state normalized innovation square and reverse predicted state normalized innovation square，corrects predicted state on-line by noise model adjustment when the normalized innovation square ratio is greater than the threshold.Radar target tracking simulation results show that the algorithm can improve filtering accuracy and robustness when target maneuvers and noise increases.

摘要： 标准卡尔曼滤波算法对系统的数学模型和噪声统计特性进行了假设，当该假设与实际的模型不匹配时容易造成滤波误差较大甚至滤波发散。提出基于反向预测卡尔曼滤波自适应算法，通过比较原始预测状态归一化新息平方和反向预测状态归一化新息平方，当比值大于设定阈值时在线进行过程噪声调整，从而修正预测状态。雷达目标跟踪仿真研究结果表明，该算法对目标机动和过程噪声增大有较强的自适应性，能够提高滤波精度和鲁棒性。