Unifying consensus and covariance intersection for decentralized state estimation
Unifying consensus and covariance intersection for decentralized state estimation
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DOI：
10.1109/iros.2016.7759044
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发表时间：
2016-10
期刊：
影响因子：
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通讯作者：
A. Tamjidi;S. Chakravorty;Dylan A. Shell
中科院分区：
文献类型：
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作者：
A. Tamjidi;S. Chakravorty;Dylan A. Shell
本文提出了一种新的用于分散动态估计的递归信息共识过滤器。假设局部估计器只能访问局部信息，并且没有假设有关通信网络拓扑的结构，通信网络不需要始终连接。迭代协方差交集 (ICI) 用于对可能相关的先验达成共识，而对新信息的共识则使用基于 Metropolis Hastings Markov Chain (MHMC) 的权重来处理。我们建立了估计性能的界限，并表明我们的方法产生了优于 CI 的无偏保守估计。对所提出的方法的性能进行了评估，并与大气色散问题上的竞争算法进行了比较。
This paper presents a new recursive information consensus filter for decentralized dynamic-state estimation. Local estimators are assumed to have access only to local information and no structure is assumed about the topology of the communication network, which need not be connected at all times. Iterative Covariance Intersection (ICI) is used to reach consensus over priors which might become correlated, while consensus over new information is handled using weights based on a Metropolis Hastings Markov Chain (MHMC). We establish bounds for estimation performance and show that our method produces unbiased conservative estimates that are better than CI. The performance of the proposed method is evaluated and compared with competing algorithms on an atmospheric dispersion problem.