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Decomposed CNN for Sub-Nyquist Tensor-Based 2-D DOA Estimation
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DOI：10.1109/LSP.2023.3282815.png)
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摘要
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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.
Keyword:
Coarray tensor
convolution kernel decomposition
DOA estimation
sub-Nyquist tensor
期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
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引用论文
Coupled Coarray Tensor CPD for DOA Estimation With Coprime L-Shaped ArrayCo-array张量CPD与co-rime l形阵列的DOA估计
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