一种新的无序量测处理算法

A New Algorithm for Out of Sequence Measurement Problem

在中心式多传感器目标跟踪系统中,当从不同的传感器发送量测到处理中心时,会出现不同的时间延迟。这导致源自同样目标的量测会出现无序到达中心的现象,由此产生无序量测处理问题。该文受分布式/航迹融合理论中"中心式估计的重构"思想启发,通过组合前向预测与等价量测方法,提出一种新的处理无序量测的方法,该方法涉及到状态估计的去相关问题。最后通过理论分析和仿真试验表明:该算法对于一步延迟是最优的,且当过程噪声很小,系统航迹的更新速率相当高时,该算法表现出的性能下降是很小的。

In multisensor tracking systems that operate in a centralized manner, there are usually different time delays in transmitting the scans or frames from the various sensors to the center. This can lead to situations where measurements from the same target arrive out of sequence. Thus, such ＂Out-Of-Sequence＂ Measurement （OOSM） problem is result in. Inherited from the idea of ＂reconstruction of Centralized Estimate＂, a new algorithm for out of sequence measurement problem, by fusing forward prediction and equivalent measurement approach, is presented. The algorithm involves a decorrelation problem. Theoretical Analysis and Monte Carlo Simulation results indicate that the new algorithm corresponding to the 1-step lag OOSM case is optimal, and when the process noise is small, and/or the update rate of system tracks is reasonably high, the degradation in performance of the algorithm has been shown to be small.