Unsupervised Learning Strategy for Direction-of-Arrival Estimation Network

Yuan, Ye; Wu, Shuang; Wu, Meiping; Yuan, Naichang

doi:10.1109/lsp.2021.3096117

Cited by 23 publications

(9 citation statements)

References 34 publications

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“…One of the solutions to this problem is using the synthesized data that well capture the characteristics of the realistic wave. Another solution is to use unsupervised learning such as [ 20 ]. Unsupervised learning can make collecting data much easier since data labeling is not required.…”

Section: Resultsmentioning

confidence: 99%

“…After the introduction of deep learning [ 15 ], DoA estimation algorithms based on various types of neural network (NN) have been proposed in [ 16 , 17 , 18 , 19 , 20 ]. One of the benefits of using deep learning is that the user does not have to know the exact signal model if there are sufficient training data.…”

Section: Introductionmentioning

confidence: 99%

“…The CNN-based DoA estimation shows better estimation accuracy and resolution compared to the DNN-based DoA estimation. In [ 20 ], the DoA estimation based on unsupervised learning was proposed, where unsupervised learning can make data collection easier since data labeling is not required. In recent works, there have been efforts to exploit features of classical DoA estimation, rather than solely depending on NN.…”

Section: Introductionmentioning

confidence: 99%

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Leveraging Deep Learning for Practical DoA Estimation: Experiments with Real Data Collected via USRP

Chung

Park

Kim

2022

Sensors

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This paper presents an experimental validation of deep learning-based direction-of-arrival (DoA) estimation by using realistic data collected via universal software radio peripheral (USRP). Deep neural network (DNN) and convolutional neural network (CNN) structures are designed to estimate the DoA. Two types of data are used for training networks. One is the data synthesized by the signal model, and the other is the data collected by USRP. Here, the signal model considers both mutual coupling and multipath signals. Experimental results show that the estimation performance is most accurate when training DNN and CNN with the collected data. Furthermore, the estimation tends to be poor in the indoor environment, which suffers from the strong non-line-of-sight (NLoS) signals.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Leveraging Deep Learning for Practical DoA Estimation: Experiments with Real Data Collected via USRP

Chung

Park

Kim

2022

Sensors

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“…The second domain is based on unsupervised learning. For instance, Yuan et al (2021) proposed an unsupervised learning strategy for DOA estimation using a novel loss function. Although methods based on deep learning provide significant improvements in the estimation results, they cannot be easily generalized to the underwater environment, which is complex and exhibits temporal and spatial variations.…”

Section: Deep Learning-based Doa Estimation Methodsmentioning

confidence: 99%

Deep learning-based DOA estimation using CRNN for underwater acoustic arrays

Chen²,

Bai³

et al. 2022

Front. Mar. Sci.

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In the marine environment, estimating the direction of arrival (DOA) is challenging because of the multipath signals and low signal-to-noise ratio (SNR). In this paper, we propose a convolutional recurrent neural network (CRNN)-based method for underwater DOA estimation using an acoustic array. The proposed CRNN takes the phase component of the short-time Fourier transform of the array signals as the input feature. The convolutional part of the CRNN extracts high-level features, while the recurrent component captures the temporal dependencies of the features. Moreover, we introduce a residual connection to further improve the performance of DOA estimation. We train the CRNN with multipath signals generated by the BELLHOP model and a uniform line array. Experimental results show that the proposed CRNN yields high-accuracy DOA estimation at different SNR levels, significantly outperforming existing methods. The proposed CRNN also exhibits a relatively short processing time for DOA estimation, extending its applicability.

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“…Among them, a new unsupervised DNN neural network learning strategy for incoherent DOA is proposed in document [ 14 ], which can improve the degree of freedom of the array while maintaining a certain accuracy. In document [ 15 ], a DNN network framework composed of a multitask automatic encoder and a series of parallel multi-layer classifiers is proposed.…”

Section: Introductionmentioning

confidence: 99%

Small Sample Coherent DOA Estimation Method Based on S2S Neural Network Meta Reinforcement Learning

Wang

2023

Sensors

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Aiming at the existing Direction of Arrival (DOA) methods based on neural network, a large number of samples are required to achieve signal-scene adaptation and accurate angle estimation. In the coherent signal environment, the problems of a larger amount of training sample data are required. In this paper, the DOA of coherent signal is converted into the DOA parameter estimation of the angle interval of incident signal. The accurate estimation of coherent DOA under the condition of small samples based on meta−reinforcement learning (MRL) is realized. The meta−reinforcement learning method in this paper models the process of angle interval estimation of coherent signals as a Markov decision process. In the inner loop layer, the sequence to sequence (S2S) neural network is used to express the angular interval feature sequence of the incident signal DOA. The strategy learning of the existence of angle interval under small samples is realized through making full use of the context relevance of spatial spectral sequence through S2S neural network. Thus, according to the optimal strategy, the output sequence is sequentially determined to give the angle interval of the incident signal. Finally, DOA is obtained through one-dimensional spectral peak search according to the angle interval obtained. The experiment shows that the meta−reinforcement learning algorithm based on S2S neural network can quickly converge to the optimal state by only updating the gradient of S2S neural network parameters with a small sample set when a new signal environment appears.

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Unsupervised Learning Strategy for Direction-of-Arrival Estimation Network

Cited by 23 publications

References 34 publications

Leveraging Deep Learning for Practical DoA Estimation: Experiments with Real Data Collected via USRP

Leveraging Deep Learning for Practical DoA Estimation: Experiments with Real Data Collected via USRP

Deep learning-based DOA estimation using CRNN for underwater acoustic arrays

Small Sample Coherent DOA Estimation Method Based on S2S Neural Network Meta Reinforcement Learning

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