文章基本信息

标题：Large-Scale Distributed Kalman Filtering via an Optimization Approach
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作者：Mathias Hudoba de Badyn ; Mehran Mesbahi
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2017
卷号：50
期号：1
页码：10742-10747
DOI：10.1016/j.ifacol.2017.08.2268
语种：English
出版社：Elsevier
摘要：AbstractLarge-scale distributed systems such as sensor networks, often need to achieve filtering and consensus on an estimated parameter from high-dimensional measurements. Running a Kalman filter on every node in such a network is computationally intensive; in particular the matrix inversion in the Kalman gain update step is expensive. In this paper, we extend previous results in distributed Kalman filtering and large-scale machine learning to propose a gradient descent step for updating an estimate of the error covariance matrix; this is then embedded and analyzed in the context of distributed Kalman filtering. We provide properties of the resulting filters, in addition to a number of applications throughout the paper.
关键词：KeywordsMachine learningfast Kalman algorithmsstate estimationgradient methods