2D DOA Estimation for Multiple Parallel Sparse Linear Arrays via Recursive Roots Finding
Yaxing Yue, Chengwei Zhou, Ying Liu, Fuquan Nie, Jun Pan, Yong Wang, Zhiguo Shi · IEEE Transactions on Vehicular Technology · 2025
Multiple parallel sparse linear arrays (MPSLAs) can be strategically deployed in two-dimensional (2D) or threedimensional (3D) space, offering a unique advantage by enabling easy conformal design and enhancing the degrees-of-freedom (DoFs) for 2D direction-of-arrival (DOA) estimation. This paper delves into an efficient 2D DOA estimation approach, providing specific estimation formulas using the 2D sparse parallel planar array and 3D sparse parallel cylindrical array as illustrative examples. We first transform the 2D DOA estimation problem into a recursive roots finding-based one-dimensional (1D) DOA estimation problem by leveraging the parallel nature of the considered arrays, where we, for the first time, establish polynomial coefficients in a recursive fashion. Our proposed approach has no constraints on the number of subarray counts. Subsequently, the paired 1D DOA estimation in the other dimension is realized based on the Rayleigh-Ritz theorem. Simulation results show the advantages of the proposed approach in terms of DoFs, estimation accuracy, and computational complexity when compared to current state-of-the-art approaches