Sparse-Optimized Nested L-Shaped Arrays with Hybrid LASSO-PSCM Framework for Enhanced DOA Estimation in Mutual Coupling Environments

This paper proposes three novel sparse-optimized nested L-shaped array (LA) configurations (ISNA-LA, ANCADiS-LA, AINA-LA) to address the challenges of limited degrees of freedom (DOFs) and mutual coupling in conventional LAs. These configurations optimize element place-ment in two-level nested arrays (TLNAs) to enhance DOFs and reduce mutual coupling via sparse spatial distribution. A hybrid 2-D DOA estimation framework integrating the least absolute shrinkage and selection operator (LASSO) and the pair-matching method by signal covariance matrices (PSCM) is established, enabling full utilization of difference coarray DOFs and automatic azimuth-elevation pairing. Numerical simulations show that the proposed arrays outperform conventional LAs in DOFs and mutual coupling reduction, while the LASSO-PSCM algorithm achieves superior 2-D estimation accuracy.