Tensorized Neural Layer Decomposition for 2-D DOA Estimation
Loading...
Access rights
openAccess
acceptedVersion
URL
Journal Title
Journal ISSN
Volume Title
A4 Artikkeli konferenssijulkaisussa
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
Date
2023
Major/Subject
Mcode
Degree programme
Language
en
Pages
5
Series
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings ; Volume 2023-June
Abstract
Existing matrix-based neural network for direction-of-arrival (DOA) estimation has to train a large amount of parameters proportional to the length of vectorized signal statistics, resulting in a heavy system overload. To address the problem, a tensorized neural layer decomposition-based neural network is proposed for 2-D DOA estimation. In particular, the covariance tensor of tensor signals is propagated to hidden state tensors. The feedforward propagation is formulated as an inverse Tucker decomposition, such that parameters in the tensorized neural layers are compressed into inverse Tucker factors. Accordingly, the tensorized backpropagation procedure is designed for network training. It is proved that the number of parameters is significantly reduced, which leads to a faster training process. Simulation results demonstrate that the proposed method reduces the number of trained parameters by more than 122,000 times compared to the matrix-based neural network while maintaining a moderate accuracy.Description
Funding Information: The work of H. Zheng, C. Zhou, and Z. Shi was supported in part by the National Natural Science Foundation of China (No. 62271444, U21A20456), the Zhejiang Provincial Natural Science Foundation of China (No. LZ23F010007), the Zhejiang University Education Foundation Qizhen Scholar Foundation, and the 5G Open Laboratory of Hangzhou Future Sci-Tech City. The work of S. A. Vorobyov was supported in part by the Academy of Finland fund (No. 357715). H. Zheng is supported by the China Scholarship Council for his visit at Aalto University. Publisher Copyright: © 2023 IEEE.
Keywords
Covariance tensor, DOA estimation, neural layer decomposition, tensorized neural network
Other note
Citation
Zheng, H, Zhou, C, Vorobyov, S A & Shi, Z 2023, Tensorized Neural Layer Decomposition for 2-D DOA Estimation . in ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings . ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2023-June, IEEE, IEEE International Conference on Acoustics, Speech, and Signal Processing, Rhodes Island, Greece, 04/06/2023 . https://doi.org/10.1109/ICASSP49357.2023.10095288