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

TL;DR

This paper introduces an enhanced coarray transformation model and a novel list-based maximum likelihood algorithm for improved direction-of-arrival estimation using non-uniform linear arrays, outperforming existing methods.

Contribution

It presents a new EDCTM approach and LBML-OMP algorithm that improve estimation accuracy and computational efficiency in NLA-based DOA estimation.

Findings

EDCTM provides better estimates than existing models.

LBML-OMP outperforms current sparse recovery algorithms.

Simulation results confirm improved accuracy and efficiency.

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.