基于伪测量的分布式最优单步延迟航迹融合估计

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-sequencemeasurements，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 tacks，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．

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

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.