A Deep Learning Based Mutual Coupling Correction and DOA Estimation Algorithm

Fang, Wenwei; Yu, Dingke; Wang, Xin; Xi, Yuzhang; Cao, Zhihui; Song, Chunyi; Xu, Zhiwei

doi:10.1109/wcsp52459.2021.9613199

Cited by 5 publications

(1 citation statement)

References 19 publications

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“…ResNet is a good solution to the problem of gradient disappearance and gradient explosion in deep networks, and the problem of network degradation is solved to some extent by using the residual structure. Fang et al [27] and Li and Tian [28] also implemented DOA estimation using ResNet. They essentially did not change the previous training approach, which is limited by the acquisition of labeled datasets.…”

Section: Introductionmentioning

confidence: 99%

A Gridless DOA Estimation Method for Sparse Sensor Array

Gao,

Ma,

Liu

et al. 2023

Remote Sensing

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Direction-of-arrival (DOA) estimation is still a pivotal research direction in array signal processing. Traditional algorithms based on the signal subspace and compressed sensing theory usually suffer from off-grid and computational complexity. Deep-learning-based methods usually face difficulty in obtaining labeled datasets. With the development of array technology, sparse sensor arrays can effectively reduce the number of sensors, which in turn reduces the complexity of the hardware. Therefore, effective DOA estimation algorithms for sparse sensor arrays need to be further investigated. An unsupervised deep learning method is proposed here to address the above issues. A training model was built based on the residual network structure. The DOA estimation was implemented using Vandermonde decomposition. Finally, the experimental findings confirmed the efficacy of the proposed algorithms presented in this article.

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Section: Introductionmentioning

confidence: 99%