Decomposed CNN for Sub-Nyquist Tensor-Based 2-D DOA Estimation
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2023
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en
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5
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IEEE Signal Processing Letters, Volume 30, pp. 708-712
Abstract
Direction-of-arrival (DOA) estimation using sub-Nyquist tensor signals benefits from enhanced performance by extracting structural angular information with multi-dimensional sparse arrays. Although convolutional neural network (CNN) has been employed to achieve efficient DOA estimation in challenging conditions, conventional methods demand excessive memory storage and computation power to process sub-Nyquist tensor statistics. In this letter, we propose a decomposed CNN for sub-Nyquist tensor-based 2-D DOA estimation, where an augmented coarray tensor is derived and used as the network input. To compress convolution kernels for efficient coarray tensor propagation, we develop a convolution kernel decomposition approach. This enables the acquisition of canonical polyadic (CP) factors containing compressed parameters. Performing decomposable convolution between the coarray tensor and the CP factors leads to resource-efficient DOA estimation. Our simulation results indicate that the proposed method conserves system resources while maintaining competitive performance.Description
Publisher Copyright: IEEE
Keywords
Array signal processing, Coarray tensor, Convolution, convolution kernel decomposition, Convolutional neural networks, Direction-of-arrival estimation, DOA estimation, Estimation, Kernel, sub-Nyquist tensor, Tensors
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Citation
Zheng, H, Zhou, C, Vorobyov, S, Wang, Q & Shi, Z 2023, ' Decomposed CNN for Sub-Nyquist Tensor-Based 2-D DOA Estimation ', IEEE Signal Processing Letters, vol. 30, pp. 708-712 . https://doi.org/10.1109/LSP.2023.3282815