Optimal centralized update with multiple local out-of-sequence measurements
Optimal centralized update with multiple local out-of-sequence measurements
复制标题
DOI：
10.1109/ical.2008.4636360
复制
发表时间：
2008-09
期刊：
影响因子：
--
通讯作者：
Xiaojing Shen;Yunmin Zhu;Zhisheng You;Enbin Song
中科院分区：
文献类型：
--
作者：
Xiaojing Shen;Yunmin Zhu;Zhisheng You;Enbin Song
在多传感器目标跟踪系统中，由传感器产生的观测通常不按顺序到达中央处理器。对于单次失序测量（OOSM），已有一些更新算法。在本文中，我们考虑最佳的集中式更新算法与多个异步（不同的滞后时间）OOSM。首先，我们将文献[2]中的单阶滞后OOSM最优更新算法推广到多阶滞后OOSM最优集中式更新算法。然后，基于最佳线性无偏估计，我们提出了一个最优的集中更新算法的多个任意步长滞后OOSM。
In multisensor target tracking systems, observations produced by sensors typically arrive at a central processor out of sequence. There have been some update algorithms for single out-of-sequence measurement (OOSM). In this paper, we consider optimal centralized update algorithms with multiple asynchronous (different lag time) OOSMs. Firstly, we generalize the optimal update algorithm with single 1-step-lag OOSM in [2] to optimal centralized update algorithm with multiple 1-step-lag OOSMs. Then, based on best linear unbiased estimation, we present an optimal centralized update algorithm with multiple arbitrary-step-lag OOSMs.