Study of List-Based OMP and an Enhanced Model for Direction Finding with Non-Uniform Arrays

• URL: http://arxiv.org/abs/2105.03774v1
• Date: Sat, 8 May 2021 20:43:13 GMT
• Title: Study of List-Based OMP and an Enhanced Model for Direction Finding with Non-Uniform Arrays
• Authors: W. S. Leite and R. C. de Lamare
• Abstract summary: This paper proposes an enhanced coarray transformation model (EDCTM) and a mixed greedy maximum likelihood called List-Based Likelihood Orthogonal Matching Pursuit (LBML-OMP) 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper 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 non-uniform 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.

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