Study of List-Based OMP and an Enhanced Model for Direction Finding with Non-Uniform Arrays
This work addresses direction finding in sensor arrays, which is incremental with specific algorithmic enhancements for non-uniform arrays.
The paper tackles direction-of-arrival estimation for non-uniform linear arrays by proposing an enhanced coarray transformation model (EDCTM) and a List-Based Maximum Likelihood Orthogonal Matching Pursuit (LBML-OMP) algorithm, resulting in improved performance over existing methods as shown in simulations.
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.