Recognizing Non-Collaborative Radio Station Communication Behaviors Using an Ameliorated LeNet
This work improves a LeNet model algorithm based on a signal’s bispectral features to recognize the communication behaviors of a non-collaborative short-wave radio station. At first, the mapping relationships between the burst waveforms and the communication behaviors of a radio station are analyzed...
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| Veröffentlicht in: | Sensors (Basel, Switzerland) Switzerland), 2020-08, Vol.20 (15), p.4320 |
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| creator | Wu, Zilong Chen, Hong Lei, Yingke |
| description | This work improves a LeNet model algorithm based on a signal’s bispectral features to recognize the communication behaviors of a non-collaborative short-wave radio station. At first, the mapping relationships between the burst waveforms and the communication behaviors of a radio station are analyzed. Then, bispectral features of simulated behavior signals are obtained as the input of the network. With regard to the recognition neural network, the structure of LeNet and the size of the convolutional kernel in LeNet are optimized. Finally, the five types of communication behavior are recognized by using the improved bispectral estimation matrix of signals and the ameliorated LeNet. The experimental results show that when the signal-to-noise ratio (SNR) values are 8, 10, or 15 dB, the recognition accuracy values of the improved algorithm reach 81.5%, 94.5%, and 99.3%, respectively. Compared with other algorithms, the training time cost and recognition accuracy of the proposed algorithm are lower and higher, respectively; thus, the proposed algorithm is of great practical value. |
| doi_str_mv | 10.3390/s20154320 |
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At first, the mapping relationships between the burst waveforms and the communication behaviors of a radio station are analyzed. Then, bispectral features of simulated behavior signals are obtained as the input of the network. With regard to the recognition neural network, the structure of LeNet and the size of the convolutional kernel in LeNet are optimized. Finally, the five types of communication behavior are recognized by using the improved bispectral estimation matrix of signals and the ameliorated LeNet. The experimental results show that when the signal-to-noise ratio (SNR) values are 8, 10, or 15 dB, the recognition accuracy values of the improved algorithm reach 81.5%, 94.5%, and 99.3%, respectively. Compared with other algorithms, the training time cost and recognition accuracy of the proposed algorithm are lower and higher, respectively; thus, the proposed algorithm is of great practical value.</description><identifier>ISSN: 1424-8220</identifier><identifier>EISSN: 1424-8220</identifier><identifier>DOI: 10.3390/s20154320</identifier><identifier>PMID: 32756394</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Algorithms ; Armed forces ; Behavior ; bispectrum estimation ; Collaboration ; communication behaviors ; Computer simulation ; convolutional neural network (CNN) ; Fault diagnosis ; Feature recognition ; Image retrieval ; Mapping ; Methods ; Neural networks ; Noise levels ; Radio stations ; Sensors ; Short wave radio transmission ; short-wave radio station ; signal recognition ; Signal to noise ratio ; Verbal communication ; Waveforms</subject><ispartof>Sensors (Basel, Switzerland), 2020-08, Vol.20 (15), p.4320</ispartof><rights>2020. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). 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At first, the mapping relationships between the burst waveforms and the communication behaviors of a radio station are analyzed. Then, bispectral features of simulated behavior signals are obtained as the input of the network. With regard to the recognition neural network, the structure of LeNet and the size of the convolutional kernel in LeNet are optimized. Finally, the five types of communication behavior are recognized by using the improved bispectral estimation matrix of signals and the ameliorated LeNet. The experimental results show that when the signal-to-noise ratio (SNR) values are 8, 10, or 15 dB, the recognition accuracy values of the improved algorithm reach 81.5%, 94.5%, and 99.3%, respectively. 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| subjects | Algorithms Armed forces Behavior bispectrum estimation Collaboration communication behaviors Computer simulation convolutional neural network (CNN) Fault diagnosis Feature recognition Image retrieval Mapping Methods Neural networks Noise levels Radio stations Sensors Short wave radio transmission short-wave radio station signal recognition Signal to noise ratio Verbal communication Waveforms |
| title | Recognizing Non-Collaborative Radio Station Communication Behaviors Using an Ameliorated LeNet |
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