Power spectrum estimation in innovation models

AKÇAY H., TÜRKAY S.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING, vol.121, pp.227-245, 2019 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 121
  • Publication Date: 2019
  • Doi Number: 10.1016/j.ymssp.2018.11.026
  • Journal Name: MECHANICAL SYSTEMS AND SIGNAL PROCESSING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.227-245
  • Keywords: System identification, Power spectrum, Subspace method, Nuclear norm, Regularization, Innovation model, FREQUENCY-DOMAIN, SYSTEM-IDENTIFICATION, SUBSPACE ALGORITHMS, RANK MINIMIZATION, NORM, REGULARIZATION, APPROXIMATION, VARIABLES
  • Anadolu University Affiliated: Yes

Abstract

In this paper, a regularized nuclear norm minimization-based subspace identification algorithm is proposed to estimate discrete-time power spectra of multi-input/multi-out systems in innovation models from output-only time-domain measurements. Parametrization and implementation issues are carefully addressed. The new identification algorithm and three popular subspace algorithms are tested on a numerical example and three real-life applications drawn from automotive engineering, acoustics, and renewable energy. (C) 2018 Elsevier Ltd. All rights reserved.