  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.
