Optimal sample-based fusion for distributed state estimation
Optimal sample-based fusion for distributed state estimation
复制标题
用于分布式状态估计的基于最优样本的融合
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
U. Hanebeck
中科院分区：
文献类型：
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作者：
Jannik Steinbring;B. Noack;Marc Reinhardt;U. Hanebeck
本文提出了一种在线性和非线性系统的分布式状态估计中最优融合估计的新方法。最佳融合需要知道本地获得的估计之间的正确相关性。计算正确相关性的简单而棘手的方法是在所有节点之间交换关于每个处理的测量的信息。相反，我们建议通过在每个节点上保持和处理与实际局部状态估计并行的一小部分确定性样本来获得正确的相关性。除了局部状态估计之外，将这些样本发送到融合中心允许正确地重建所有估计之间的期望相关性。在这样做时，每个节点不需要关于在其他节点上处理的测量的任何信息。通过跟踪多摄像机网络中的扩展目标，证明了该方法的最优性。
In this paper, we present a novel approach to optimally fuse estimates in distributed state estimation for linear and nonlinear systems. An optimal fusion requires the knowledge of the correct correlations between locally obtained estimates. The naive and intractable way of calculating the correct correlations would be to exchange information about every processed measurement between all nodes. Instead, we propose to obtain the correct correlations by keeping and processing a small set of deterministic samples on each node in parallel to the actual local state estimation. Sending these samples in addition to the local state estimate to the fusion center allows for correctly reconstructing the desired correlations between all estimates. In doing so, each node does not need any information about measurements processed on other nodes. We show the optimality of the proposed method by means of tracking an extended object in a multi-camera network.