Low Complexity Single-Snapshot DoA Estimation via Bayesian Compressive Sensing

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The problem of single-snapshot direction of arrival (DoA) estimation with antenna arrays has been considered. A sectorized approach based on Bayesian Compressive Sensing (BCS) has been proposed. In this method, the angular space is discretized, defining many non-overlapping small grids which cover the desired large angular space. First, a BCS estimation is run in each of the sectors to estimate the DoA of the signals. Then, a second stage is performed to correct the inconsistencies at the edges due to signal leaking between sectors. The performance of the method has been analyzed via extensive Monte-Carlo simulations in which the number of targets, their Radar Cross Section (RCS), and their location have been varied in a large extent, and the targets were observed by a Frequency Modulated Continuous Wave (FMCW) radar with an 86-element Uniform Linear Array (ULA). The results are compared with state-of-the-art methods in terms of estimation accuracy and resolution. Moreover, an analysis of the computational time, critical for many real-time applications, is presented, which shows a reduction of 20 times in the computational time compared with the standard BCS. Finally, the method has also been validated using experimental data collected with a commercial automotive radar.
Original languageEnglish
Title of host publicationProceedings of the 2023 IEEE Radar Conference (RadarConf23)
Place of PublicationPiscataway
Number of pages6
ISBN (Electronic)978-1-6654-3669-4
ISBN (Print)978-1-6654-3670-0
Publication statusPublished - 2023
Event2023 IEEE Radar Conference (RadarConf23) - San Antonio, United States
Duration: 1 May 20235 May 2023


Conference2023 IEEE Radar Conference (RadarConf23)
Country/TerritoryUnited States
CitySan Antonio

Bibliographical note

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  • Bayesian compressive sensing (BCS)
  • direction of-arrival estimation (DoA)
  • antenna arrays


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