基于伪测量的分布式最优单步延迟航迹融合估计

Distributed optimal one-step-lag estimation for out-of-sequence track fusion based on pseudo-measurement for multi-sensor system

融合中心如何处理无序局部数据,对分布式多传感器系统的运行品质至关重要.本文将系统中的局部估计转化为伪测量,将分布式融合估计转化为二级集中式融合估计.将所得的伪测量兼分布式融合估计算法与单步延迟的无序测量数据(out-of-sequence measurements,OOSM)最优滤波—A1算法进行组合,得出了分布式多传感器系统的最优单步延迟无序航迹(out-of-sequence tracks,OOST)估计算法,适用于航迹无序局部数据融合估计.该算法具有最优估计性能.

The local processing of out-of-sequence data is crucial to the operation quality of the distributed multisensor system. We treat the estimation in local nodes as the pseudo-measurement, and convert the distributed estimation to a two-level centralized fusion estimation. By combining the pseudo-measurement-distributed fusion estimation algorithm obtained above with the optimal one-step-lag out-of-sequence measurement（OOSM） filter, we develop the optimal onestep-lag out-of-sequence track（OOST） estimation algorithm. This algorithm can be applied to the fusion estimation of out-of-sequence data of tracks with optimal estimation performances.