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CDC
2015
IEEE

Accuracy improvement in Least-Squares estimation with harmonic regressor: New preconditioning and correction methods

3 years 8 months ago
Accuracy improvement in Least-Squares estimation with harmonic regressor: New preconditioning and correction methods
— Numerical aspects of least squares estimation have not been sufficiently studied in the literature. In particular, information matrix has a large condition number for systems with harmonic regressor in the initial steps of RLS (Recursive Least Squares) estimation. A large condition number indicates invertibility problems and necessitates the development of new algorithms with improved accuracy of estimation. Symmetric and positive definite information matrix is presented in a block diagonal form in this paper using transformation, which involves the Schur complement. Block diagonal sub-matrices have significantly smaller condition numbers and therefore can be easily inverted, forming a preconditioner for a large scale system. High order algorithms with controllable accuracy are used for solving least squares estimation problem. The second part of the paper is devoted to the performance improvement in classical RLS algorithm, which represents a feedforward estimation procedure wi...
Alexander Stotsky
Added 18 Apr 2016
Updated 18 Apr 2016
Type Journal
Year 2015
Where CDC
Authors Alexander Stotsky
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