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• W3112969745 abstract "On-grid based direction-of-arrival (DOA) estimation methods rely on the resolution of a difficult group-sparse optimization problem that involves the $ell _{2,0}$ pseudo-norm. In this work, we show that an exact relaxation of this problem can be obtained by replacing the $ell _{2,0}$ 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 $ell _{2,0}$ -regularized criteria. We then show on numerical simulations that the minimization of the proposed relaxation with an iteratively reweighted $ell _{2,1}$ algorithm leads to an improved performance over traditional approaches." @default.
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• W3112969745 date "2021-01-01" @default.
• W3112969745 modified "2023-10-04" @default.
• W3112969745 title "Direction-of-Arrival Estimation Through Exact Continuous ℓ_2,0-Norm Relaxation" @default.
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• W3112969745 doi "https://doi.org/10.1109/lsp.2020.3042771" @default.
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