TY - GEN
T1 - Deep Root Music Algorithm for Data-Driven Doa Estimation
AU - Shmuel, Dor H.
AU - Merkofer, Julian P.
AU - Revach, Guy
AU - Van Sloun, Ruud J.G.
AU - Shlezinger, Nir
N1 - Publisher Copyright:
© 2023 IEEE.
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.
KW - deep learning
KW - DoA estimation
UR - http://www.scopus.com/inward/record.url?scp=86000372170&partnerID=8YFLogxK
U2 - 10.1109/ICASSP49357.2023.10096504
DO - 10.1109/ICASSP49357.2023.10096504
M3 - Conference contribution
AN - SCOPUS:86000372170
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
BT - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
PB - Institute of Electrical and Electronics Engineers
T2 - 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023
Y2 - 4 June 2023 through 10 June 2023
ER -