Deep Beamforming for Joint Direction of Arrival Estimation and Source Detection

• Published in: 2022 56th Asilomar Conference on Signals, Systems, and Computers pp. 1403 - 1407
• Main Authors: Chaudhari, Shreyas, Moura, Jose M.F.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 31-10-2022
• Subjects: Antenna measurements; Array Processing; Computational modeling; Deep learning; Direction of Arrival; Direction-of-arrival estimation; Sonar measurements; Transmitting antennas; Wireless communication
• ISSN: 2576-2303
• DOI: 10.1109/IEEECONF56349.2022.10052106

Direction of arrival (DoA) estimation is a well studied problem with several significant applications in radar, sonar, wireless communications, and audio signal processing. A majority of conventional algorithms for DoA estimation require prior knowledge of the number of transmitters and/or sufficient measurements for estimating the received signal covariance matrix. When these requirements are not satisfied, the performance of such algorithms degrades considerably. Recently, some deep learning-based approaches to direction of arrival estimation have been proposed. However, similar to conventional algorithms, most of these methods require the number of transmitters to be known a priori or require a large number of snapshots. We propose a new deep learning approach to DoA estimation. Our approach is inspired by conventional beamforming-based methods and identifies both the number of transmitting sources as well as their angular positions. We demonstrate empirically that our method outperforms conventional methods and a recently proposed deep learning approach in the low-SNR and low-snapshot regimes.