DA-MUSIC: Data-Driven DoA Estimation via Deep Augmented MUSIC Algorithm

Julian P. Merkofer, Guy Revach, Nir Shlezinger, Tirza Routtenberg, Ruud J.G. van Sloun

Research output: Contribution to journalArticlepeer-review


Direction of arrival (DoA) estimation of multiple signals is pivotal in sensor array signal processing. A popular multisignal DoA estimation method is the multiple signal classification (MUSIC) algorithm, which enables high-performance superresolution DoA recovery while being highly applicable in practice. MUSIC is a model-based algorithm, relying on an accurate mathematical description of the relationship between the signals and the measurements and assumptions on the signals themselves (non-coherent, narrowband sources). As such, it is sensitive to model imperfections. In this work, we propose to overcome these limitations of MUSIC by augmenting the algorithm with specifically designed neural architectures. Our proposed deep augmented MUSIC (DA-MUSIC) algorithm is thus a hybrid model-based/data-driven DoA estimator, which leverages data to improve performance and robustness while preserving the interpretable flow of the classic method. DA-MUSIC is shown to learn to overcome limitations of the purely model-based method, such as its inability to successfully localize coherent sources as well as estimate the number of coherent signal sources present.

Original languageEnglish
Pages (from-to)1-14
Number of pages14
JournalIEEE Transactions on Vehicular Technology
StatePublished - 28 Sep 2023


  • Broadband communication
  • Covariance matrices
  • Direction-of-arrival estimation
  • Estimation
  • Multiple signal classification
  • Narrowband
  • Signal processing algorithms

ASJC Scopus subject areas

  • Automotive Engineering
  • Aerospace Engineering
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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