DOA Estimation Method Based on Improved Deep Convolutional Neural Network
For the multi-target DOA estimation problem of uniform linear arrays, this paper proposes a DOA estimation method based on the deep convolution neural network. The algorithm adopts the deep convolutional neural network, and the DOA estimation problem of the array signal is transformed into the inver...
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| Vydáno v: | Sensors (Basel, Switzerland) Ročník 22; číslo 4; s. 1305 |
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| Abstract | For the multi-target DOA estimation problem of uniform linear arrays, this paper proposes a DOA estimation method based on the deep convolution neural network. The algorithm adopts the deep convolutional neural network, and the DOA estimation problem of the array signal is transformed into the inverse mapping problem of the array output covariance matrix to a binary sequence in which “1” indicates that there is a target incident in the corresponding angular direction at that position. The upper triangular array of the discrete covariance matrix is used as the data input to realize the DOA estimation of multiple sources. The simulation results show that the DOA estimation accuracy of the proposed algorithm is significantly better than that of the typical super-resolution estimation algorithm under the conditions of low SNR and small snapshot. Under the conditions of high SNR and large snapshot, the estimation accuracy of the proposed algorithm is basically the same as those of the MUSIC algorithm, ESPRIT algorithm, and ML algorithm, which are better than that of the deep fully connected neural network. The analysis of the simulation results shows that the algorithm is effective, and the time and space complexity can be further reduced by replacing the square array with the upper triangular array as the input. |
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| AbstractList | For the multi-target DOA estimation problem of uniform linear arrays, this paper proposes a DOA estimation method based on the deep convolution neural network. The algorithm adopts the deep convolutional neural network, and the DOA estimation problem of the array signal is transformed into the inverse mapping problem of the array output covariance matrix to a binary sequence in which "1" indicates that there is a target incident in the corresponding angular direction at that position. The upper triangular array of the discrete covariance matrix is used as the data input to realize the DOA estimation of multiple sources. The simulation results show that the DOA estimation accuracy of the proposed algorithm is significantly better than that of the typical super-resolution estimation algorithm under the conditions of low SNR and small snapshot. Under the conditions of high SNR and large snapshot, the estimation accuracy of the proposed algorithm is basically the same as those of the MUSIC algorithm, ESPRIT algorithm, and ML algorithm, which are better than that of the deep fully connected neural network. The analysis of the simulation results shows that the algorithm is effective, and the time and space complexity can be further reduced by replacing the square array with the upper triangular array as the input.For the multi-target DOA estimation problem of uniform linear arrays, this paper proposes a DOA estimation method based on the deep convolution neural network. The algorithm adopts the deep convolutional neural network, and the DOA estimation problem of the array signal is transformed into the inverse mapping problem of the array output covariance matrix to a binary sequence in which "1" indicates that there is a target incident in the corresponding angular direction at that position. The upper triangular array of the discrete covariance matrix is used as the data input to realize the DOA estimation of multiple sources. The simulation results show that the DOA estimation accuracy of the proposed algorithm is significantly better than that of the typical super-resolution estimation algorithm under the conditions of low SNR and small snapshot. Under the conditions of high SNR and large snapshot, the estimation accuracy of the proposed algorithm is basically the same as those of the MUSIC algorithm, ESPRIT algorithm, and ML algorithm, which are better than that of the deep fully connected neural network. The analysis of the simulation results shows that the algorithm is effective, and the time and space complexity can be further reduced by replacing the square array with the upper triangular array as the input. For the multi-target DOA estimation problem of uniform linear arrays, this paper proposes a DOA estimation method based on the deep convolution neural network. The algorithm adopts the deep convolutional neural network, and the DOA estimation problem of the array signal is transformed into the inverse mapping problem of the array output covariance matrix to a binary sequence in which “1” indicates that there is a target incident in the corresponding angular direction at that position. The upper triangular array of the discrete covariance matrix is used as the data input to realize the DOA estimation of multiple sources. The simulation results show that the DOA estimation accuracy of the proposed algorithm is significantly better than that of the typical super-resolution estimation algorithm under the conditions of low SNR and small snapshot. Under the conditions of high SNR and large snapshot, the estimation accuracy of the proposed algorithm is basically the same as those of the MUSIC algorithm, ESPRIT algorithm, and ML algorithm, which are better than that of the deep fully connected neural network. The analysis of the simulation results shows that the algorithm is effective, and the time and space complexity can be further reduced by replacing the square array with the upper triangular array as the input. |
| Audience | Academic |
| Author | Zhao, Fangzheng Zhan, Chenghong Zhang, Yule Hu, Guoping |
| AuthorAffiliation | 1 Graduate School, Air Force Engineering University, Xi’an 710043, China; zhaofz1020@163.com (F.Z.); chenghong_zhan@163.com (C.Z.); yule_zhang0921@163.com (Y.Z.) 2 Air and Missile Defense College, Air Force Engineering University, Xi’an 710043, China |
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| Author_xml | – sequence: 1 givenname: Fangzheng orcidid: 0000-0003-0421-5917 surname: Zhao fullname: Zhao, Fangzheng – sequence: 2 givenname: Guoping surname: Hu fullname: Hu, Guoping – sequence: 3 givenname: Chenghong surname: Zhan fullname: Zhan, Chenghong – sequence: 4 givenname: Yule orcidid: 0000-0002-6515-8189 surname: Zhang fullname: Zhang, Yule |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35214207$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1007/s10489-020-01678-4 10.1109/ICASSP.2015.7178838 10.1109/WASPAA.2017.8170010 10.1109/29.7543 10.1007/978-1-4842-5364-9 10.1109/29.32276 10.3390/s20185431 10.1109/TSP.2013.2289875 10.3390/s20010081 10.1016/j.flowmeasinst.2020.101804 10.1007/978-3-319-73074-5 10.1109/ICASSP.2015.7178484 10.1109/78.97999 10.1109/TSP.2021.3053495 10.1109/TAP.1986.1143830 10.1016/j.sigpro.2013.03.009 10.1007/978-1-4471-5571-3_4 10.1049/PBRA026E 10.1109/ACCESS.2020.2966653 10.1109/TSP.2003.820089 |
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| SubjectTerms | Algorithms Computer Simulation covariance matrix deep convolutional neural network Deep learning DOA estimation Methods Neural networks Neural Networks, Computer Noise Propagation Signal processing the upper triangular matrix |
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| Title | DOA Estimation Method Based on Improved Deep Convolutional Neural Network |
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