Robust and Bayesian Subspace Identification

• 成果类型: Article
• 署名作者: Mesquita, Alexandre R.
• 署名单位: Universidade Federal de Minas Gerais
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2024.3465560
• 发表日期: 2025
• 页码: 1395-1401
• 关键词: estimation; Bayes methods; noise; Matrix decomposition; Noise level; Singular value decomposition; Noise measurement; Bayesian estimation; shrinkage estimators; subspace system identification

摘要

Model estimates obtained from traditional subspace identification methods may be subject to significant variance. This elevated variance is aggravated in the cases of high-dimensional models, limited sample size, or high noise level. Common solutions in statistics to reduce the effect of variance are regularized estimators, shrinkage estimators, and Bayesian estimation. In the current work, we investigate the latter two solutions, which are relatively unexplored in subspace identification methods. Our experimental results, from a large random sample of system models, show that our proposed estimators reduce the median of estimation risks by 10% compared with traditional subspace methods. In the case of large measurement noise, this median estimation risk was reduced by 34%.