Deep Root Music Algorithm for Data-Driven Doa Estimation

Dor H. Shmuel; Julian P. Merkofer; Guy Revach; Ruud J.G. Van Sloun; Nir Shlezinger

doi:10.1109/ICASSP49357.2023.10096504

Deep Root Music Algorithm for Data-Driven Doa Estimation

Dor H. Shmuel, Julian P. Merkofer, Guy Revach, Ruud J.G. Van Sloun, Nir Shlezinger

Department of Electrical & Computer Engineering

Research output: Contribution to journal › Conference article › peer-review

11 Scopus citations

Abstract

Direction of arrival (DoA) estimation is a fundamental task in array processing. A popular family of DoA estimation algorithms are subspace methods, which operate by dividing the measurements into distinct signal and noise subspaces. Subspace methods, such as Root-MUSIC, require the sources to be non-coherent, and are considerably degraded when this does not hold. In this work we propose Deep Root-MUSIC (DR-MUSIC); a data-driven DoA estimator which augments Root-MUSIC with a deep neural network applied to the empirical autocorrelation of the input. DR-MUSIC learns how to divide the observations into distinguishable subspaces, thus leveraging data to cope with coherent sources, low SNR and limited snapshots, while preserving the interpretability and the suitability of the model-based algorithm.

Original language	English
Journal	Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
DOIs	https://doi.org/10.1109/ICASSP49357.2023.10096504
State	Published - 1 Jan 2023
Event	48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023 - Rhodes Island, Greece Duration: 4 Jun 2023 → 10 Jun 2023

Keywords

DoA estimation
deep learning

ASJC Scopus subject areas

Software
Signal Processing
Electrical and Electronic Engineering

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10.1109/ICASSP49357.2023.10096504

Cite this

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title = "Deep Root Music Algorithm for Data-Driven Doa Estimation",

abstract = "Direction of arrival (DoA) estimation is a fundamental task in array processing. A popular family of DoA estimation algorithms are subspace methods, which operate by dividing the measurements into distinct signal and noise subspaces. Subspace methods, such as Root-MUSIC, require the sources to be non-coherent, and are considerably degraded when this does not hold. In this work we propose Deep Root-MUSIC (DR-MUSIC); a data-driven DoA estimator which augments Root-MUSIC with a deep neural network applied to the empirical autocorrelation of the input. DR-MUSIC learns how to divide the observations into distinguishable subspaces, thus leveraging data to cope with coherent sources, low SNR and limited snapshots, while preserving the interpretability and the suitability of the model-based algorithm.",

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AU - Shmuel, Dor H.

AU - Merkofer, Julian P.

AU - Revach, Guy

AU - Van Sloun, Ruud J.G.

AU - Shlezinger, Nir

PY - 2023/1/1

Y1 - 2023/1/1

N2 - Direction of arrival (DoA) estimation is a fundamental task in array processing. A popular family of DoA estimation algorithms are subspace methods, which operate by dividing the measurements into distinct signal and noise subspaces. Subspace methods, such as Root-MUSIC, require the sources to be non-coherent, and are considerably degraded when this does not hold. In this work we propose Deep Root-MUSIC (DR-MUSIC); a data-driven DoA estimator which augments Root-MUSIC with a deep neural network applied to the empirical autocorrelation of the input. DR-MUSIC learns how to divide the observations into distinguishable subspaces, thus leveraging data to cope with coherent sources, low SNR and limited snapshots, while preserving the interpretability and the suitability of the model-based algorithm.

AB - Direction of arrival (DoA) estimation is a fundamental task in array processing. A popular family of DoA estimation algorithms are subspace methods, which operate by dividing the measurements into distinct signal and noise subspaces. Subspace methods, such as Root-MUSIC, require the sources to be non-coherent, and are considerably degraded when this does not hold. In this work we propose Deep Root-MUSIC (DR-MUSIC); a data-driven DoA estimator which augments Root-MUSIC with a deep neural network applied to the empirical autocorrelation of the input. DR-MUSIC learns how to divide the observations into distinguishable subspaces, thus leveraging data to cope with coherent sources, low SNR and limited snapshots, while preserving the interpretability and the suitability of the model-based algorithm.

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KW - deep learning

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DO - 10.1109/ICASSP49357.2023.10096504

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JF - Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing

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Y2 - 4 June 2023 through 10 June 2023

ER -

Deep Root Music Algorithm for Data-Driven Doa Estimation

Abstract

Keywords

ASJC Scopus subject areas

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