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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE signal processing letters Jg. 28; S. 16 - 20
Hauptverfasser: Soubies, Emmanuel, Chinatto, Adilson, Larzabal, Pascal, Romano, Joao M. T., Blanc-Feraud, Laure
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2020.3042771