TY - GEN
T1 - Deep Learning Based Source Direction Estimation with Magnitude-only Array Measurements
AU - Kuang, Jingdong
AU - Liu, Wei
AU - Wan, Zhengyu
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024/7
Y1 - 2024/7
N2 - Most DOA estimation techniques require phase information of the received array signals to accurately estimate the direction of arrival (DOA). Nevertheless, in some scenarios, the phase information may not be easily accessible or reliable due to various reasons such as hardware limitations or calibration issues. One way to tackle this challenge is to discard the phase information or only measure the magnitude of the received signals. In this work, a deep-learning (DL) based DOA estimation method is proposed for effective DOA estimation with magnitude-only measurements. To improve the generalization ability and robustness of the proposed solution, an attention mechanism is employed, and to avoid the implicit assumption that the number of signals is known in the training process, labels of the data are converted into the one-hot form. Simulation results show that the proposed solution has superior performance in terms of computational complexity, accuracy, and robustness compared to traditional DOA estimation algorithms.
AB - Most DOA estimation techniques require phase information of the received array signals to accurately estimate the direction of arrival (DOA). Nevertheless, in some scenarios, the phase information may not be easily accessible or reliable due to various reasons such as hardware limitations or calibration issues. One way to tackle this challenge is to discard the phase information or only measure the magnitude of the received signals. In this work, a deep-learning (DL) based DOA estimation method is proposed for effective DOA estimation with magnitude-only measurements. To improve the generalization ability and robustness of the proposed solution, an attention mechanism is employed, and to avoid the implicit assumption that the number of signals is known in the training process, labels of the data are converted into the one-hot form. Simulation results show that the proposed solution has superior performance in terms of computational complexity, accuracy, and robustness compared to traditional DOA estimation algorithms.
KW - attention mechanism
KW - deep learning
KW - Direction of arrival
KW - magnitude measurements
KW - sensor arrays
UR - http://www.scopus.com/inward/record.url?scp=85198512109&partnerID=8YFLogxK
U2 - 10.1109/ISCAS58744.2024.10558455
DO - 10.1109/ISCAS58744.2024.10558455
M3 - Conference article published in proceeding or book
AN - SCOPUS:85198512109
T3 - Proceedings - IEEE International Symposium on Circuits and Systems
BT - ISCAS 2024 - IEEE International Symposium on Circuits and Systems
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE International Symposium on Circuits and Systems, ISCAS 2024
Y2 - 19 May 2024 through 22 May 2024
ER -