Off-grid DOA Estimation Based on Analysis of the Convexity of Maximum Likelihood Function
Spatial compressive sensing (SCS) has recently been applied to direction-of-arrival (DOA) estimation owing to advantages over conventional ones. However the performance of compressive sensing (CS)-based estimation methods decreases when true DOAs are not exactly on the discretized sampling grid. We...
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ftdatacite:10.48550/arxiv.1412.6720 2023-05-15T16:01:12+02:00 Off-grid DOA Estimation Based on Analysis of the Convexity of Maximum Likelihood Function Liu, Liang Wei, Ping 2014 https://dx.doi.org/10.48550/arxiv.1412.6720 https://arxiv.org/abs/1412.6720 unknown arXiv https://dx.doi.org/10.1587/transfun.e98.a.2705 arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ Information Theory cs.IT FOS Computer and information sciences article-journal Article ScholarlyArticle Text 2014 ftdatacite https://doi.org/10.48550/arxiv.1412.6720 https://doi.org/10.1587/transfun.e98.a.2705 2022-04-01T12:30:00Z Spatial compressive sensing (SCS) has recently been applied to direction-of-arrival (DOA) estimation owing to advantages over conventional ones. However the performance of compressive sensing (CS)-based estimation methods decreases when true DOAs are not exactly on the discretized sampling grid. We solve the off-grid DOA estimation problem using the deterministic maximum likelihood (DML) estimation method. In this work, we analyze the convexity of the DML function in the vicinity of the global solution. Especially under the condition of large array, we search for an approximately convex range around the ture DOAs to guarantee the DML function convex. Based on the convexity of the DML function, we propose a computationally efficient algorithm framework for off-grid DOA estimation. Numerical experiments show that the rough convex range accords well with the exact convex range of the DML function with large array and demonstrate the superior performance of the proposed methods in terms of accuracy, robustness and speed. : 10 pages, 6 figures, 1 table. arXiv admin note: text overlap with arXiv:1108.5838 by other authors Text DML DataCite Metadata Store (German National Library of Science and Technology) |
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Open Polar |
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DataCite Metadata Store (German National Library of Science and Technology) |
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topic |
Information Theory cs.IT FOS Computer and information sciences |
spellingShingle |
Information Theory cs.IT FOS Computer and information sciences Liu, Liang Wei, Ping Off-grid DOA Estimation Based on Analysis of the Convexity of Maximum Likelihood Function |
topic_facet |
Information Theory cs.IT FOS Computer and information sciences |
description |
Spatial compressive sensing (SCS) has recently been applied to direction-of-arrival (DOA) estimation owing to advantages over conventional ones. However the performance of compressive sensing (CS)-based estimation methods decreases when true DOAs are not exactly on the discretized sampling grid. We solve the off-grid DOA estimation problem using the deterministic maximum likelihood (DML) estimation method. In this work, we analyze the convexity of the DML function in the vicinity of the global solution. Especially under the condition of large array, we search for an approximately convex range around the ture DOAs to guarantee the DML function convex. Based on the convexity of the DML function, we propose a computationally efficient algorithm framework for off-grid DOA estimation. Numerical experiments show that the rough convex range accords well with the exact convex range of the DML function with large array and demonstrate the superior performance of the proposed methods in terms of accuracy, robustness and speed. : 10 pages, 6 figures, 1 table. arXiv admin note: text overlap with arXiv:1108.5838 by other authors |
format |
Text |
author |
Liu, Liang Wei, Ping |
author_facet |
Liu, Liang Wei, Ping |
author_sort |
Liu, Liang |
title |
Off-grid DOA Estimation Based on Analysis of the Convexity of Maximum Likelihood Function |
title_short |
Off-grid DOA Estimation Based on Analysis of the Convexity of Maximum Likelihood Function |
title_full |
Off-grid DOA Estimation Based on Analysis of the Convexity of Maximum Likelihood Function |
title_fullStr |
Off-grid DOA Estimation Based on Analysis of the Convexity of Maximum Likelihood Function |
title_full_unstemmed |
Off-grid DOA Estimation Based on Analysis of the Convexity of Maximum Likelihood Function |
title_sort |
off-grid doa estimation based on analysis of the convexity of maximum likelihood function |
publisher |
arXiv |
publishDate |
2014 |
url |
https://dx.doi.org/10.48550/arxiv.1412.6720 https://arxiv.org/abs/1412.6720 |
genre |
DML |
genre_facet |
DML |
op_relation |
https://dx.doi.org/10.1587/transfun.e98.a.2705 |
op_rights |
arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
op_doi |
https://doi.org/10.48550/arxiv.1412.6720 https://doi.org/10.1587/transfun.e98.a.2705 |
_version_ |
1766397164458606592 |