Direction-of-Arrival Estimation Through Exact Continuous ℓ2,0-Norm Relaxation

On-grid based direction-of-arrival (DOA) estimation methods rely on the resolution of a difficult group-sparse optimization problem that involves the <inline-formula><tex-math notation="LaTeX">\ell _{2,0}</tex-math></inline-formula> pseudo-norm. In this work, we sho...

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Vydáno v:IEEE signal processing letters Ročník 28; s. 16 - 20
Hlavní autoři: Soubies, Emmanuel, Chinatto, Adilson, Larzabal, Pascal, Romano, Joao M. T., Blanc-Feraud, Laure
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
0}</tex-math> </inline-formula>-norm minimization
<inline-formula xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> <tex-math notation="LaTeX"> ell _{2
Algorithms
Antenna arrays
Computational geometry
Convexity
Direction of arrival
Direction-of-arrival estimation
DOA
Estimation
exact relaxations
Minimax technique
Minimization
MMV-sparse optimization
Optimization
Signal processing algorithms
Sparse matrices
ISSN:1070-9908, 1558-2361
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Abstract On-grid based direction-of-arrival (DOA) estimation methods rely on the resolution of a difficult group-sparse optimization problem that involves the <inline-formula><tex-math notation="LaTeX">\ell _{2,0}</tex-math></inline-formula> pseudo-norm. In this work, we show that an exact relaxation of this problem can be obtained by replacing the <inline-formula><tex-math notation="LaTeX">\ell _{2,0}</tex-math></inline-formula> term with a group minimax concave penalty with suitable parameters. This relaxation is more amenable to non-convex optimization algorithms as it is continuous and admits less local (not global) minimizers than the initial <inline-formula><tex-math notation="LaTeX">\ell _{2,0}</tex-math></inline-formula>-regularized criteria. We then show on numerical simulations that the minimization of the proposed relaxation with an iteratively reweighted <inline-formula><tex-math notation="LaTeX">\ell _{2,1}</tex-math></inline-formula> algorithm leads to an improved performance over traditional approaches.
AbstractList On-grid based direction-of-arrival (DOA) estimation methods rely on the resolution of a difficult group-sparse optimization problem that involves the [Formula Omitted] pseudo-norm. In this work, we show that an exact relaxation of this problem can be obtained by replacing the [Formula Omitted] term with a group minimax concave penalty with suitable parameters. This relaxation is more amenable to non-convex optimization algorithms as it is continuous and admits less local (not global) minimizers than the initial [Formula Omitted]-regularized criteria. We then show on numerical simulations that the minimization of the proposed relaxation with an iteratively reweighted [Formula Omitted] algorithm leads to an improved performance over traditional approaches.
On-grid based direction-of-arrival (DOA) estimation methods rely on the resolution of a difficult group-sparse optimization problem that involves the <inline-formula><tex-math notation="LaTeX">\ell _{2,0}</tex-math></inline-formula> pseudo-norm. In this work, we show that an exact relaxation of this problem can be obtained by replacing the <inline-formula><tex-math notation="LaTeX">\ell _{2,0}</tex-math></inline-formula> term with a group minimax concave penalty with suitable parameters. This relaxation is more amenable to non-convex optimization algorithms as it is continuous and admits less local (not global) minimizers than the initial <inline-formula><tex-math notation="LaTeX">\ell _{2,0}</tex-math></inline-formula>-regularized criteria. We then show on numerical simulations that the minimization of the proposed relaxation with an iteratively reweighted <inline-formula><tex-math notation="LaTeX">\ell _{2,1}</tex-math></inline-formula> algorithm leads to an improved performance over traditional approaches.
Author Larzabal, Pascal
Blanc-Feraud, Laure
Chinatto, Adilson
Soubies, Emmanuel
Romano, Joao M. T.
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Snippet On-grid based direction-of-arrival (DOA) estimation methods rely on the resolution of a difficult group-sparse optimization problem that involves the...
On-grid based direction-of-arrival (DOA) estimation methods rely on the resolution of a difficult group-sparse optimization problem that involves the [Formula...
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Algorithms
Antenna arrays
Computational geometry
Convexity
Direction of arrival
Direction-of-arrival estimation
DOA
Estimation
exact relaxations
Minimax technique
Minimization
MMV-sparse optimization
Optimization
Signal processing algorithms
Sparse matrices
Title Direction-of-Arrival Estimation Through Exact Continuous ℓ2,0-Norm Relaxation
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