TY - GEN
T1 - Deep Convolution Network Based Super Resolution DOA Estimation with Toeplitz and Sparse Prior
AU - Duan, Chenkang
AU - Tian, Ye
AU - Liu, Wei
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024/4
Y1 - 2024/4
N2 - In this paper, a deep learning (DL) based approach is investigated for direction-of-arrival (DOA) estimation, where large-scale uniform linear arrays (ULAs) and small number of samples are considered. Different from existing DL based DOA estimators, the proposed solution first exploits the Toeplitz prior of array covariance matrix and the linear shrinkage technique to obtain an enhanced sample covariance matrix (SCM), which is then formulated as a sparse linear representation (SLR) problem. Finally, a suitable deep convolution network (DCN) that learns such a SLR characteristic from large training dataset is designed. With aid of Toeplitz and sparse prior, the proposed solution can provide an increased resolution and estimation accuracy under the considered scenario, as verified by simulations.
AB - In this paper, a deep learning (DL) based approach is investigated for direction-of-arrival (DOA) estimation, where large-scale uniform linear arrays (ULAs) and small number of samples are considered. Different from existing DL based DOA estimators, the proposed solution first exploits the Toeplitz prior of array covariance matrix and the linear shrinkage technique to obtain an enhanced sample covariance matrix (SCM), which is then formulated as a sparse linear representation (SLR) problem. Finally, a suitable deep convolution network (DCN) that learns such a SLR characteristic from large training dataset is designed. With aid of Toeplitz and sparse prior, the proposed solution can provide an increased resolution and estimation accuracy under the considered scenario, as verified by simulations.
KW - deep convolution network
KW - Direction of arrival estimation
KW - sparse prior
KW - Toeplitz prior
UR - https://www.scopus.com/pages/publications/85195366533
U2 - 10.1109/ICASSP48485.2024.10448253
DO - 10.1109/ICASSP48485.2024.10448253
M3 - Conference article published in proceeding or book
AN - SCOPUS:85195366533
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 8581
EP - 8585
BT - 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 49th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024
Y2 - 14 April 2024 through 19 April 2024
ER -