有失序量测的随机卡尔曼滤波状态更新最优解  

The optimal update solution of random Kalman filtering with out-of-sequence measurements

作　　者：王东华[1] 朱允民[1] 

机构地区：[1]四川大学数学学院国家信息控制实验室,成都610064

出　　处：《四川大学学报（自然科学版）》2009年第5期1271-1274,共4页Journal of Sichuan University(Natural Science Edition)

基　　金：国家自然科学基金(60874107;10826101);863项目(2006AA12A104);国家信息控制实验室基金

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时当前状态的最优更新估计方程.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.

关 键 词：随机参数矩阵卡尔曼滤波 精确状态更新方程 线性估计理论 失序量测 

分 类 号：O211.64[理学—概率论与数理统计]