Subspace Identification of Linear Systems with Observation Outliers

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  • TANAKA Hideyuki
    Department of Applied Mathematics and Physics, Graduate School of Informatics, Kyoto University
  • ALMUTAWA Jaafar
    Department of Applied Mathematics and Physics, Graduate School of Informatics, Kyoto University
  • KATAYAMA Tohru
    Department of Applied Mathematics and Physics, Graduate School of Informatics, Kyoto University

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  • 出力に異常値を含む線形システムの部分空間同定法
  • シュツリョク ニ イジョウチ オ フクム センケイ システム ノ ブブン クウカン ドウテイホウ

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Abstract

In this paper, we consider a subspace identification method for linear stochastic systems subject to observation outliers, where the observation noise contains large values with a low probability. We derive a subspace identification method by combining the orthogonal decomposition-based subspace identification method (ORT-method) and a weighted LQ decomposition. We apply the ORT-method to the input-output data, coupled with the standard LQ decomposition to obtain residuals of the output sequence. By using the median of residuals, outliers are detected by a simple scheme in robust statistics. Based on detected outliers, a weighting matrix is generated automatically, and is incorporated in the weighted LQ decomposition to get an improved estimate of the system matrices. A numerical example is included to show the effectiveness of the proposed method.

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