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着重对红外跟踪系统目标状态估计过程中存在的观测量滞后和相关性问题进行研究。在目标跟踪过程中,脱靶量信号的滞后导致目标量测位置和真实值间是有误差的,进而通过状态估计得到的目标参数也是不准确的,另外观测量的相关性也降低了估计效果的准确性。对于以上问题,首先利用观测重组技术对时滞观测序列进行重组,将其转化为变结构无时滞观测系统,并在此基础上提出了一种时滞椭球集员估计算法(Delayed ellipsoidal set filter,DESF)。仿真实验结果表明,DESF算法可以有效克服观测序列相关性的影响,同时系统跟踪性能较不考虑观测延迟的情况也有显著提高。
Focus on the infrared tracking system target state estimation process exists in the observation lag and correlation issues. In the process of target tracking, the lag of the miss distance signal leads to the error between the target measurement position and the real value, and the target parameter obtained through the state estimation is also inaccurate, and the correlation of the observation also reduces the estimation effect accuracy. For the above problems, the observational and reorganization technique is first used to reorganize the time series observation system and transform it into a variable structure time delay observation system. Based on this, a delayed ellipsoidal set estimation algorithm (Delayed ellipsoidal set filter, DESF). The simulation results show that the DESF algorithm can effectively overcome the influence of the correlation of observation sequence, and the tracking performance of the system has also been significantly improved compared with the observation delay.