中文摘要：

在中心式多传感器跟踪系统中，经常会出现同一目标的量测没有按照正常的时间顺序到达处理中心的现象．如何利用（相对当前最新时刻而言）负时点的失序量测本更新状态的问题在现实的多传感器系统中普遍存在．对于具有确定性参数矩阵的卡尔曼滤波，Bar—Shalom于2002年给出了利用失序量测的最优状态更新估计方程．本文作者将此结果进一步推广到了具有随机参数矩阵的卡尔曼滤波，给出了利用失序量测对当前状态的最优更新估计方程．

英文摘要：

In multisensor tracking systems that operate in a centralized manner, there are usually situations where sensor measurements of the same target arrive to the center out of sequence. The question of how to use ＂negative-time （relative to the latest time） measurements＂ to update the latest state estimate is quite common issue in real multisensor systems. In the deterministic parameter matrices Kalman filtering, Bar-Shalom presented the optimal state estimate update equation of using out-of-sequence measurements in 2002. As a generalization, the optimal state estimate update of random parameters matrices Kalman filtering with out-of-sequence measurements is presented in this paper.