Journal Article

Efficient gridless 2D DOA estimation based on generalized matrix‐form atomic norm minimization

Wang, Muhan, Zhang, Zhe, Zhang, Bingchen, Wu, Yirong, Gao, Silin

2024

Overview

Two‐dimensional (2D) direction‐of‐arrival (DOA) estimation is crucial in array signal processing. Compressed sensing (CS) provides a superior alternative to spatial spectrum estimation algorithms by enabling 2D DOA estimation of correlated sources from single snapshot data. However, the grid mismatch effect inherent in grid‐based CS algorithms impacts estimation accuracy. Despite recent advancements, the state‐of‐the‐art gridless CS algorithm, decoupled atomic norm minimization, is limited to specific 2D array geometries, such as uniform rectangular arrays. This letter presents an efficient gridless 2D DOA estimation algorithm for generalized rectangular arrays, including both uniform and sparse arrays. The proposed algorithm achieves high accuracy through a novel approach called generalized matrix‐form atomic norm minimization and provides a fast solution using the alternating direction method of multipliers. Validation through computer simulations and practical experiments underscores its efficacy. This letter presents an efficient gridless Two‐dimensional direction‐of‐arrival estimation algorithm for generalized rectangular arrays, including both uniform and sparse arrays. By introducing generalized matrix‐form atomic norm minimization, the applicability of atomic norm techniques are extended to a broader range of array geometries. Combining generalized matrix‐form atomic norm minimization with the alternating direction method of multipliers, the approach achieves both high accuracy and computational efficiency.

• Publisher: Wiley
• Subject: array signal processing / compressed sensing / direction‐of‐arrival estimation / radar signal processing