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On sparsity‐inducing methods in system identification and state estimation
Author(s) -
Bako Laurent
Publication year - 2022
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.5995
Subject(s) - estimator , univariate , computer science , robustness (evolution) , system identification , identification (biology) , multivariate statistics , algorithm , pattern recognition (psychology) , data mining , artificial intelligence , mathematics , machine learning , statistics , biology , biochemistry , chemistry , botany , gene , measure (data warehouse)
The purpose of this article is to survey some sparsity‐inducing methods in system identification and state estimation. Such methods can be divided into two main categories: methods inducing sparsity in the parameters and those sparsifying the prediction error. In the last class we discuss in particular the least absolute deviation estimator and its robustness properties with respect to sparse noise in both cases of univariate and multivariate measurements. We also discuss the application of sparsity‐inducing methods to switched system identification and to state estimation for linear systems in the presence sparse and dense measurement noises. While the presentation focuses essentially on bridging some existing results, some technical refinements, and new features are also provided.