List-Based OMP and an Enhanced Model for DOA Estimation With Nonuniform Arrays

This article proposes an enhanced coarray transformation model (EDCTM) and a mixed greedy maximum likelihood algorithm called list-based maximum likelihood orthogonal matching pursuit (LBML-OMP) for direction-of-arrival estimation with nonuniform linear arrays (NLAs). The proposed EDCTM approach obt...

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Bibliographic Details
Published in:IEEE transactions on aerospace and electronic systems 2021-12, Vol.57 (6), p.4457-4464
Main Authors: Leite, Wesley S., Lamare, Rodrigo C. de
Format: Article
Language:English
Subjects:
Compressive sensing
Covariance matrices
Dictionaries
Direction of arrival
Direction-of-arrival estimation
Greedy algorithms
Indexing
Iterative methods
Linear arrays
Matched pursuit
Matching pursuit algorithms
Maximum likelihood estimation
nonuniform linear arrays
orthogonal matching pursuit
Sensor arrays
Signal classification
sparse recovery
Spatial smoothing
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Summary:This article proposes an enhanced coarray transformation model (EDCTM) and a mixed greedy maximum likelihood algorithm called list-based maximum likelihood orthogonal matching pursuit (LBML-OMP) for direction-of-arrival estimation with nonuniform linear arrays (NLAs). The proposed EDCTM approach obtains improved estimates when Khatri-Rao product-based models are used to generate difference coarrays under the assumption of uncorrelated sources. In the proposed LBML-OMP technique, for each iteration a set of candidates is generated based on the correlation-maximization between the dictionary and the residue vector. LBML-OMP then chooses the best candidate based on a reduced-complexity asymptotic maximum likelihood decision rule. Simulations show the improved results of EDCTM over existing approaches and that LBML-OMP outperforms existing sparse recovery algorithms as well as spatial smoothing multiple signal classification with NLAs.
ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2021.3087836