Deep Learning Approach in DOA Estimation: A Systematic Literature Review

Ge, Shengguo; Li, Kuo; Rum, Siti Nurulain Mohd

doi:10.1155/2021/6392875

Cited by 24 publications

(6 citation statements)

References 57 publications

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“…However, this type of method is often reserved for specific types of arrays and is difficult to apply widely. In recent years, with the application of deep learning, DOA estimation methods based on deep learning have emerged [ 15 ]. Fast matrix decomposition (eigenvalue decomposition and unitary decomposition) was achieved by Luo and Gohil et al, using deep learning, and DOA estimation was then accomplished using conventional algorithms [ 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

Research on Underdetermined DOA Estimation Method with Unknown Number of Sources Based on Improved CNN

Zhao

Zhou

et al. 2023

Sensors

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This paper proposes a joint estimation method for source number and DOA based on an improved convolutional neural network for unknown source number and undetermined DOA estimation. By analyzing the signal model, the paper designs a convolutional neural network model based on the existence of a mapping relationship between the covariance matrix and both the source number and DOA estimation. The model, which discards the pooling layer to avoid data loss and introduces the dropout method to improve generalization, takes the signal covariance matrix as input and the two branches of source number estimation and DOA estimation as outputs, and achieves the unfixed number of DOA estimation by filling in invalid values. Simulation experiments and analysis of the results show that the algorithm can effectively achieve the joint estimation of source number and DOA. Under the conditions of high SNR and a large snapshot number, both the proposed algorithm and the traditional algorithm have high estimation accuracy, while under the conditions of low SNR and a small snapshot, the algorithm is better than the traditional algorithm, and under the underdetermined conditions, where the traditional algorithm often fails, the algorithm can still achieve the joint estimation.

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Section: Introductionmentioning

confidence: 99%

Research on Underdetermined DOA Estimation Method with Unknown Number of Sources Based on Improved CNN

Zhao

Zhou

et al. 2023

Sensors

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“…With the development of deep learning (DL), the DOA estimation algorithm based on DL has excellent performance [19][20][21][22][23]. A deep network with fully connected (FC) layers for only two sources has also been proposed to realize DOA estimation [22].…”

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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“…In recent years, there have been some deep learning-based methods in DOA estimation. However, the application of deep learning in DOA estimation is not yet widespread [17]. Recently, works have mainly used deep neural networks (DNN) [16,18,19] and convolutional neural networks (CNN) [20][21][22][23] to construct DOA estimation models.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent DOA Estimation Error Calibration Method Based on Transfer Learning

et al. 2022

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Affected by various error factors in the actual environment, the accuracy of the direction of arrival (DOA) estimation algorithm will greatly decrease during an application. To address this issue, in this paper, we propose an intelligent DOA estimation error calibration method based on transfer learning, which learns error knowledge from a small number of actual signal samples and improves the DOA estimation accuracy in the real application. We constructed a deep convolutional neural network (CNN)-based intelligent DOA estimation model to learn the mapping between the input signals and their azimuths. We generated a large number of ideal simulation signal samples to train the CNN model and used it as the pretrained model. Then, we fine-tuned the CNN model with a small number of actual signal samples collected in the actual environment. We demonstrate the effectiveness of the proposed method through simulation experiments. The experimental results indicate that the proposed method can effectively improve the accuracy of DOA estimation in the actual environment.

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Deep Learning Approach in DOA Estimation: A Systematic Literature Review

Cited by 24 publications

References 57 publications

Research on Underdetermined DOA Estimation Method with Unknown Number of Sources Based on Improved CNN

Research on Underdetermined DOA Estimation Method with Unknown Number of Sources Based on Improved CNN

A Gridless DOA Estimation Method for Sparse Sensor Array

An Intelligent DOA Estimation Error Calibration Method Based on Transfer Learning

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