Phase enhancement model based on supervised convolutional neural network for coherent DOA estimation
When the elevation of targets is smaller than beamwidth, the coherent multi-path signals will significantly degrade the direction of arrival (DOA) estimation accuracy of existing methods for a very-high-frequency (VHF) radar system. Through detailed theoretical analysis, we demonstrate that the phas...
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| Veröffentlicht in: | Applied intelligence (Dordrecht, Netherlands) Netherlands), 2020-08, Vol.50 (8), p.2411-2422 |
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| creator | Xiang, Houhong Chen, Baixiao Yang, Ting Liu, Dong |
| description | When the elevation of targets is smaller than beamwidth, the coherent multi-path signals will significantly degrade the direction of arrival (DOA) estimation accuracy of existing methods for a very-high-frequency (VHF) radar system. Through detailed theoretical analysis, we demonstrate that the phase distortion is the key factor of degrading the accuracy of DOA estimation. Hence, a novel phase enhancement model based on supervised convolutional neural network (CNN) for coherent DOA estimation is proposed to mitigate the phase distortion and improve estimation accuracy. The results of simulation experiments and real data have demonstrated the superiority of proposed method in DOA estimation accuracy and resolution compared to classic physics-driven methods. Moreover, the proposed scheme is suitable for the coherent DOA estimation compared with existing data-driven methods. |
| doi_str_mv | 10.1007/s10489-020-01678-4 |
| format | Article |
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Through detailed theoretical analysis, we demonstrate that the phase distortion is the key factor of degrading the accuracy of DOA estimation. Hence, a novel phase enhancement model based on supervised convolutional neural network (CNN) for coherent DOA estimation is proposed to mitigate the phase distortion and improve estimation accuracy. The results of simulation experiments and real data have demonstrated the superiority of proposed method in DOA estimation accuracy and resolution compared to classic physics-driven methods. 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| subjects | Accuracy Artificial Intelligence Artificial neural networks Coherence Computer Science Computer simulation Direction of arrival Machines Manufacturing Mechanical Engineering Neural networks Phase distortion Processes Radar equipment Very high frequencies |
| title | Phase enhancement model based on supervised convolutional neural network for coherent DOA estimation |
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