由于通信链路的随机时间延迟和星上传感器测量的预处理时间的不同等因素,导致在目标飞行器的测量中产生无序量测现象。为解决该问题,提出了一种基于扩展卡尔曼滤波的前向预测多步滞后无序量测处理算法。该算法首先采用扩展卡尔曼滤波算法估算出目标飞行器的状态方程和协方差方程,然后在滤波过程中利用前向滤波更新的方法,将协方差方程更新结果去相关后,累积到当前协方差方程滤波结果中,从而有效解决了目标飞行器测量中的无序量测问题。最后,将该算法与扩展卡尔曼滤波算法、丢弃算法进行了对比仿真。仿真结果表明,采用该算法处理目标飞行器的位置和飞行速度,得到的测量误差较小,在整个观测时间内,测量误差的收敛性较好,能够实现对目标飞行器的精确测量和跟踪。 Due to the random time delay of the communication link and the pretreatment time of the on-board sensor measurement, the unordered measurement phenomenon occurs in the measurement of the target aircraft. In order to solve this problem, an algorithm based on extended Kalman filter is proposed to measure the out-of-sequence measurement of multi-step and multi-step backward prediction. The algorithm first uses the extended Kalman filter algorithm to estimate the state equation and the covariance equation of the target aircraft, and then uses the forward filtering update method in the filtering process to correlate the update result of the covariance equation and then accumulates the current covariance equation Filtering results, so as to effectively solve the problem of unordered measurement in the target aircraft measurement. Finally, the algorithm is compared with extended Kalman filter algorithm and discard algorithm. The simulation results show that the proposed algorithm can process the target aircraft’s position and flight speed, and the measurement error is small. The convergence of the measurement error is good throughout the observation time, which can accurately measure and track the target aircraft.