Reweighted Covariance Fitting Based on Nonconvex Schatten-p Minimization for Gridless Direction of Arrival Estimation
In this paper, we reformulate the gridless direction of arrival (DoA) estimation problem in a novel reweighted covariance fitting (CF) method. The proposed method promotes joint sparsity among different snapshots by means of nonconvex Schatten-p quasi-norm penalty. Furthermore, for more tractable an...
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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2020/3012952 |
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doaj-37382a20ef9d45bc8052558675f70b5b2020-11-25T03:02:48ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472020-01-01202010.1155/2020/30129523012952Reweighted Covariance Fitting Based on Nonconvex Schatten-p Minimization for Gridless Direction of Arrival EstimationLama Zien Alabideen0Oumayma Al-Dakkak1Khaldoun Khorzom2Department of Telecommunication, Higher Institute for Applied Sciences and Technology, Damascus 31983, SyriaDepartment of Telecommunication, Higher Institute for Applied Sciences and Technology, Damascus 31983, SyriaDepartment of Telecommunication, Higher Institute for Applied Sciences and Technology, Damascus 31983, SyriaIn this paper, we reformulate the gridless direction of arrival (DoA) estimation problem in a novel reweighted covariance fitting (CF) method. The proposed method promotes joint sparsity among different snapshots by means of nonconvex Schatten-p quasi-norm penalty. Furthermore, for more tractable and scalable optimization problem, we apply the unified surrogate for Schatten-p quasi-norm with two-factor matrix norms. Then, a locally convergent iterative reweighted minimization method is derived and solved efficiently via a semidefinite program using the optimization toolbox. Finally, numerical simulations are carried out in the background of unknown nonuniform noise and under the consideration of coprime array (CPA) structure. The results illustrate the superiority of the proposed method in terms of resolution, robustness against nonuniform noise, and correlations of sources, in addition to its applicability in a limited number of snapshots.http://dx.doi.org/10.1155/2020/3012952 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Lama Zien Alabideen Oumayma Al-Dakkak Khaldoun Khorzom |
spellingShingle |
Lama Zien Alabideen Oumayma Al-Dakkak Khaldoun Khorzom Reweighted Covariance Fitting Based on Nonconvex Schatten-p Minimization for Gridless Direction of Arrival Estimation Mathematical Problems in Engineering |
author_facet |
Lama Zien Alabideen Oumayma Al-Dakkak Khaldoun Khorzom |
author_sort |
Lama Zien Alabideen |
title |
Reweighted Covariance Fitting Based on Nonconvex Schatten-p Minimization for Gridless Direction of Arrival Estimation |
title_short |
Reweighted Covariance Fitting Based on Nonconvex Schatten-p Minimization for Gridless Direction of Arrival Estimation |
title_full |
Reweighted Covariance Fitting Based on Nonconvex Schatten-p Minimization for Gridless Direction of Arrival Estimation |
title_fullStr |
Reweighted Covariance Fitting Based on Nonconvex Schatten-p Minimization for Gridless Direction of Arrival Estimation |
title_full_unstemmed |
Reweighted Covariance Fitting Based on Nonconvex Schatten-p Minimization for Gridless Direction of Arrival Estimation |
title_sort |
reweighted covariance fitting based on nonconvex schatten-p minimization for gridless direction of arrival estimation |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2020-01-01 |
description |
In this paper, we reformulate the gridless direction of arrival (DoA) estimation problem in a novel reweighted covariance fitting (CF) method. The proposed method promotes joint sparsity among different snapshots by means of nonconvex Schatten-p quasi-norm penalty. Furthermore, for more tractable and scalable optimization problem, we apply the unified surrogate for Schatten-p quasi-norm with two-factor matrix norms. Then, a locally convergent iterative reweighted minimization method is derived and solved efficiently via a semidefinite program using the optimization toolbox. Finally, numerical simulations are carried out in the background of unknown nonuniform noise and under the consideration of coprime array (CPA) structure. The results illustrate the superiority of the proposed method in terms of resolution, robustness against nonuniform noise, and correlations of sources, in addition to its applicability in a limited number of snapshots. |
url |
http://dx.doi.org/10.1155/2020/3012952 |
work_keys_str_mv |
AT lamazienalabideen reweightedcovariancefittingbasedonnonconvexschattenpminimizationforgridlessdirectionofarrivalestimation AT oumaymaaldakkak reweightedcovariancefittingbasedonnonconvexschattenpminimizationforgridlessdirectionofarrivalestimation AT khaldounkhorzom reweightedcovariancefittingbasedonnonconvexschattenpminimizationforgridlessdirectionofarrivalestimation |
_version_ |
1715319819659640832 |