Fully Decentralized Estimation Using Square-Root Decompositions
Fully Decentralized Estimation Using Square-Root Decompositions
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DOI：
10.23919/fusion45008.2020.9190294
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
Susanne Radtke;B. Noack;U. Hanebeck
中科院分区：
文献类型：
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作者：
Susanne Radtke;B. Noack;U. Hanebeck
由多个空间分布的传感器节点组成的网络在许多应用中是有用的。虽然分布式信息处理比集中式过滤更健壮和灵活，但它需要仔细考虑本地状态估计之间的依赖关系。本文提出了一种算法，以保持在分散的系统中的依赖关系的跟踪，没有专门的融合中心。具体而言，它解决了由于中间融合结果导致的测量信息的重复计数以及由于共同过程噪声和共同先验信息导致的相关性。为了限制融合所需的数据量，本文引入了一种对相关性进行部分约束的方法，在减少所需数据量的同时，使融合结果更加保守。仿真研究比较了该算法的性能和收敛速度，以其他国家的最先进的方法。
Networks consisting of several spatially distributed sensor nodes are useful in many applications. While distributed processing of information can be more robust and flexible than centralized filtering, it requires careful consideration of dependencies between local state estimates. This paper proposes an algorithm to keep track of dependencies in decentralized systems where no dedicated fusion center is present. Specifically, it addresses double counting of measurement information due to intermediate fusion results as well as correlations due to common process noise and common prior information. To limit the necessary amount of data, this paper introduces a method to bound correlations partially, leading to a more conservative fusion result while reducing the necessary amount of data. Simulation studies compare the performance and convergence rate of the proposed algorithm to other state-of-the-art methods.