Structured Nyquist Correlation Reconstruction for DOA Estimation With Sparse Arrays

• Published in: IEEE transactions on signal processing Vol. 71; pp. 1849 - 1862
• Main Authors: Zhou, Chengwei, Gu, Yujie, Shi, Zhiguo, Haardt, Martin
• Format: Journal Article
• Language: English
• Published: New York IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Angles (geometry); Array signal processing; Arrays; Configurations; Correlation; Covariance matrices; Covariance matrix; Cramer-Rao bounds; Direction of arrival; Direction-of-arrival estimation; Estimation; Incoherence; Interpolation; Missing data; Nyquist spatial filling; Reconstruction; Sensor arrays; Sensors; sparse arrays; structured correlation reconstruction
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2023.3251110

Sparse arrays are known to achieve an increased number of degrees-of-freedom (DOFs) for direction-of-arrival (DOA) estimation, where an augmented virtual uniform array calculated from the correlations of sub-Nyquist spatial samples is processed to retrieve the angles unambiguously. Nevertheless, the geometry of the derived virtual array is dominated by the specific physical array configurations, as well as the deviation caused by the practical unforeseen circumstances such as detection malfunction and missing data, resulting in a quite sensitive model for virtual array signal processing. In this paper, we propose a novel sparse array DOA estimation algorithm via structured correlation reconstruction, where the Nyquist spatial filling is implemented on the physical array with a compressed transformation related to its equivalent filled array to guarantee the general applicability. While the unknown correlations located in the whole rows and columns of the augmented covariance matrix lead to the fact that strong incoherence property is no longer satisfied for matrix completion, the structural information is introduced as a priori to formulate the structured correlation reconstruction problem for matrix reconstruction. As such, the reconstructed covariance matrix can be effectively processed with full utilization of the achievable DOFs from the virtual array, but with a more flexible constraint on the array configuration. The described estimation problem is theoretically analyzed by deriving the corresponding Cramér-Rao bound (CRB). Moreover, we compare the derived CRB with the performance of the virtual array interpolation-based algorithm. Simulation results demonstrate the effectiveness of the proposed algorithm in terms of DOFs, resolution, and estimation accuracy.