TY - GEN
T1 - Fast Iteration Shrinkage Thresholding Unfolding Network for Acoustic Source Localization
AU - Chen, Fangchao
AU - Xiao, Youhong
AU - Yu, Liang
AU - Chen, Lin
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Fast and accurate acoustic source localization methods have essential application value in the field of aircraft. Compared with traditional model-based methods, acoustic source localization technology based on deep learning shows a good application prospect. However, the uninterpretability of deep learning limits the further development of this technology. This paper proposes a deep network based on fast iterative shrinkage threshold algorithm unfolding (FISTA-Net), which combines the advantages of model-based and deep learning methods. In FISTA-Net, the iterative algorithm steps are mapped into the deep network, and the model parameters can be adaptively determined through end-to-end learning. The effectiveness of the proposed method is validated by a simulated dataset for training. The results show that FISTA-Net has higher spatial resolution and accuracy in acoustic source localization than the classical deconvolution algorithms.
AB - Fast and accurate acoustic source localization methods have essential application value in the field of aircraft. Compared with traditional model-based methods, acoustic source localization technology based on deep learning shows a good application prospect. However, the uninterpretability of deep learning limits the further development of this technology. This paper proposes a deep network based on fast iterative shrinkage threshold algorithm unfolding (FISTA-Net), which combines the advantages of model-based and deep learning methods. In FISTA-Net, the iterative algorithm steps are mapped into the deep network, and the model parameters can be adaptively determined through end-to-end learning. The effectiveness of the proposed method is validated by a simulated dataset for training. The results show that FISTA-Net has higher spatial resolution and accuracy in acoustic source localization than the classical deconvolution algorithms.
KW - Acoustic Source Localization
KW - deconvolution algorithm
KW - deep learning
KW - FISTA
UR - http://www.scopus.com/inward/record.url?scp=85149928762&partnerID=8YFLogxK
U2 - 10.1109/ICICSP55539.2022.10050691
DO - 10.1109/ICICSP55539.2022.10050691
M3 - 会议稿件
AN - SCOPUS:85149928762
T3 - 2022 5th International Conference on Information Communication and Signal Processing, ICICSP 2022
SP - 784
EP - 788
BT - 2022 5th International Conference on Information Communication and Signal Processing, ICICSP 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Information Communication and Signal Processing, ICICSP 2022
Y2 - 26 November 2022 through 28 November 2022
ER -