Open AccessSelection of regularization parameter in sparse inverse problems for DOA estimation
Author(s) -
Alice Delmer,
Anne Ferréol,
Pascal Larzabal
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1476/1/012001
Subject(s) - regularization (linguistics) , eigenvalues and eigenvectors , mathematics , selection (genetic algorithm) , estimation theory , range (aeronautics) , inverse , covariance matrix , inverse problem , covariance , mathematical optimization , algorithm , computer science , statistics , artificial intelligence , mathematical analysis , physics , materials science , composite material , geometry , quantum mechanics
This article deals with the selection of the regularization parameter of a sparse ℓ 0 regularized criterion for DOA (Direction-of-Arrival) estimation. This parameter is generally empirically obtained. In this paper, we establish a theoretical range for this parameter allowing a correct estimation for a single observation. Considering that an observation is the eigenvector associated with the maximum eigenvalue of the covariance matrix, we extend the analysis to multiple observations. Numerical results support our theoretical investigations.