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Optimal Distributed Fusion Update with Same Lag Time Out-of-Sequence Measurements

机译:具有相同延迟时间失序测量的最佳分布式融合更新

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In the multisensor distributed fusion systems, observations produced by sensors typically arrive at local processors out of sequence. The resulting problem at the central processor/fusion center-how to update current estimate using multiple local out-of-sequent-measurement (OOSM) updates-is a nonstandard distributed estimation problem. In this paper, based on three update algorithms with "Out-of-Sequence" measurement (OOSM), we propose three optimal distributed fusion updates with local OOSM updates, which are, under some regularity conditions, equivalent to the centralized updates with all same lag time OOSMs respectively.
机译:在多传感器分布式融合系统中,传感器产生的观测值通常不按顺序到达本地处理器。中央处理器/融合中心所产生的问题-如何使用多个本地事后测量(OOSM)更新来更新当前估计-是非标准的分布式估计问题。在本文中,基于三种具有“失序”度量(OOSM)的更新算法,我们提出了三种具有本地OOSM更新的最优分布式融合更新,它们在某些规则性条件下等效于所有相同条件下的集中式更新滞后时间OOSM。

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