文章基本信息

标题：Common dynamic estimation via structured low-rank approximation with multiple rank constraints
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作者：Antonio Fazzi ; Nicola Guglielmi ; Ivan Markovsky 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2021
卷号：54
期号：7
页码：103-107
DOI：10.1016/j.ifacol.2021.08.342
语种：English
出版社：Elsevier
摘要：AbstractWe consider the problem of detecting the common dynamic among several observed signals. It has been shown in (Markovsky et al., 2019) that the problem is equivalent to a generalization of the classical Hankel low-rank approximation to the case of multiple rank constraints. We propose an optimization method based on the integration of ordinary differential equations describing a descent dynamic for a suitable functional to be minimized. We show how the proposed algorithm improves the numerical solutions computed by existing subspace methods which solve the same problem.
关键词：KeywordsCommon dynamicsBehavioral approachStructured low-rank approximationData-driven estimationOptimization problem