1-Bit DOA Estimation Using Expectation-Maximization Generalized Approximate Message Passing With Two L-Shaped Arrays

• Published in: IEEE communications letters Vol. 25; no. 8; pp. 2554 - 2558
• Main Authors: Li, Chenyu, Wang, Qing, Chen, Hua, Teng, Liping
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 1-bit direction of arrival (DOA); Algorithms; Analog to digital conversion; Analog to digital converters; Antennas; block sparsity; Complexity; Compressed sensing; compressed sensing (CS); Data recovery; Direction of arrival; Direction-of-arrival estimation; Estimation; Expectation-maximization generalized approximate message passing (EM-GAMP); Matching pursuit algorithms; Maximization; Message passing; Optimization; Quantization (signal); Sensors; two L-shaped arrays
• ISSN: 1089-7798; 1558-2558
• DOI: 10.1109/LCOMM.2021.3079307

The 1-Bit analog-to-digital converters can drastically reduce the complexity in sampling, storage and processing progresses, which is the urgent technology for large amount of antenna elements. In this letter, we propose a compressed sensing (CS)-based method for 1-Bit direction of arrival (DOA) estimation with two L-shaped arrays. An expectation-maximization generalized approximate message passing (EM-GAMP) algorithm is proposed, where the Gaussian mixture is utilised to model the signal's prior, and an expectation maximization (EM) method is embedded in the algorithm to iteratively estimate the unknown prior distribution. It can recovery complex-valued data directly. In addition, the block sparsity structure of sparse signal is used for solving high-dimensional DOA estimation. Simulation results demonstrate that the EM-GAMP method outperforms the other CS-based methods in terms of both DOA estimation performance and running time, especially for scenarios with large amount of antenna elements and single snapshot.