过程噪声方差实时补偿的非定轨目标跟踪

摘要

针对非定轨目标跟踪问题中过程噪声统计特性未知的特点,提出了一种实用的对过程噪声方差进行实时补偿的目标跟踪算法.该算法根据强跟踪滤波器的思想,通过实时检测新息序列来修正卡尔曼滤波算法中的状态预测误差协方差矩阵,进而对未知的过程噪声方差矩阵进行实时地补偿.由于存在新息检测机制,该算法能够有效地规避表征建模不确定性的过程噪声统计特性未知的问题,对于建模不确定性具有一定的适应能力.通过对一旋转靶标跟踪问题的仿真试验,证明了该方法的有效性.%In order to overcome the uncertainty of target motion in non-orbit target tracking problems, a practical filtering algorithm, which compensated the variance of process noises, was proposed in this work. Based on the principle of strong tracking filter, the prediction covariance in Kahnan filtering equations was modified real-time by monitoring innovation sequence in the proposed algorithm. In this way, the unknown variance of process noises was compensated effectively. Due to innovation-monitoring procedure, the proposed algorithm was adaptive to the uncertainty of target motion model. Simulation results of a rotary target tracking problem show the validity of the proposed algorithm.