Multisource DOA Estimation in Impulsive Noise Environments Using Convolutional Neural Networks
This work proposes an effective high-resolution multisource direction-of-arrival (DOA) estimation method in impulsive noise scenarios based on convolutional neural networks (CNNs). First of all, the array observation matrix is preprocessed and fed into a denoising network to suppress outliers and fi...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | International Journal of Antennas and Propagation |
| Online Access: | http://dx.doi.org/10.1155/2022/5325076 |
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| _version_ | 1832567620673470464 |
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| author | Dong Chen Young Hoon Joo |
| author_facet | Dong Chen Young Hoon Joo |
| author_sort | Dong Chen |
| collection | DOAJ |
| description | This work proposes an effective high-resolution multisource direction-of-arrival (DOA) estimation method in impulsive noise scenarios based on convolutional neural networks (CNNs). First of all, the array observation matrix is preprocessed and fed into a denoising network to suppress outliers and filter out impulsive noise. Secondly, the denoising network output is fed into a model order selection network to estimate the model order. Next, according to the estimation, the denoising network output is fed into a DOA subnetwork corresponding to the model order in a DOA network to estimate the DOA of each signal. Comprehensive simulations demonstrate that, in the presence of impulsive noise, the proposed method is effective and superior in accuracy and computation speed for multisource DOA estimation. Therefore, it is concluded that CNN can be well generalized for DOA estimation. |
| format | Article |
| id | doaj-art-cf4a97a27a484148b643bfa75eff576e |
| institution | Kabale University |
| issn | 1687-5877 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Antennas and Propagation |
| spelling | doaj-art-cf4a97a27a484148b643bfa75eff576e2025-02-03T01:01:10ZengWileyInternational Journal of Antennas and Propagation1687-58772022-01-01202210.1155/2022/5325076Multisource DOA Estimation in Impulsive Noise Environments Using Convolutional Neural NetworksDong Chen0Young Hoon Joo1School of Electronic and Information EngineeringSchool of IT Information and Control EngineeringThis work proposes an effective high-resolution multisource direction-of-arrival (DOA) estimation method in impulsive noise scenarios based on convolutional neural networks (CNNs). First of all, the array observation matrix is preprocessed and fed into a denoising network to suppress outliers and filter out impulsive noise. Secondly, the denoising network output is fed into a model order selection network to estimate the model order. Next, according to the estimation, the denoising network output is fed into a DOA subnetwork corresponding to the model order in a DOA network to estimate the DOA of each signal. Comprehensive simulations demonstrate that, in the presence of impulsive noise, the proposed method is effective and superior in accuracy and computation speed for multisource DOA estimation. Therefore, it is concluded that CNN can be well generalized for DOA estimation.http://dx.doi.org/10.1155/2022/5325076 |
| spellingShingle | Dong Chen Young Hoon Joo Multisource DOA Estimation in Impulsive Noise Environments Using Convolutional Neural Networks International Journal of Antennas and Propagation |
| title | Multisource DOA Estimation in Impulsive Noise Environments Using Convolutional Neural Networks |
| title_full | Multisource DOA Estimation in Impulsive Noise Environments Using Convolutional Neural Networks |
| title_fullStr | Multisource DOA Estimation in Impulsive Noise Environments Using Convolutional Neural Networks |
| title_full_unstemmed | Multisource DOA Estimation in Impulsive Noise Environments Using Convolutional Neural Networks |
| title_short | Multisource DOA Estimation in Impulsive Noise Environments Using Convolutional Neural Networks |
| title_sort | multisource doa estimation in impulsive noise environments using convolutional neural networks |
| url | http://dx.doi.org/10.1155/2022/5325076 |
| work_keys_str_mv | AT dongchen multisourcedoaestimationinimpulsivenoiseenvironmentsusingconvolutionalneuralnetworks AT younghoonjoo multisourcedoaestimationinimpulsivenoiseenvironmentsusingconvolutionalneuralnetworks |