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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-08-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/15/4320 |
| _version_ | 1797560482909913088 |
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| author | Zilong Wu Hong Chen Yingke Lei |
| author_facet | Zilong Wu Hong Chen Yingke Lei |
| author_sort | Zilong Wu |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T18:00:13Z |
| format | Article |
| id | doaj.art-a16c7060322541148038736d24530eff |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T18:00:13Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a16c7060322541148038736d24530eff2023-11-20T08:55:39ZengMDPI AGSensors1424-82202020-08-012015432010.3390/s20154320Recognizing Non-Collaborative Radio Station Communication Behaviors Using an Ameliorated LeNetZilong Wu0Hong Chen1Yingke Lei2College of Electronic Countermeasures, National University of Defense Technology, Hefei 230037, ChinaCollege of Electronic Countermeasures, National University of Defense Technology, Hefei 230037, ChinaCollege of Electronic Countermeasures, National University of Defense Technology, Hefei 230037, ChinaThis 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.https://www.mdpi.com/1424-8220/20/15/4320communication behaviorsbispectrum estimationsignal recognitionconvolutional neural network (CNN)short-wave radio station |
| spellingShingle | Zilong Wu Hong Chen Yingke Lei Recognizing Non-Collaborative Radio Station Communication Behaviors Using an Ameliorated LeNet Sensors communication behaviors bispectrum estimation signal recognition convolutional neural network (CNN) short-wave radio station |
| title | Recognizing Non-Collaborative Radio Station Communication Behaviors Using an Ameliorated LeNet |
| title_full | Recognizing Non-Collaborative Radio Station Communication Behaviors Using an Ameliorated LeNet |
| title_fullStr | Recognizing Non-Collaborative Radio Station Communication Behaviors Using an Ameliorated LeNet |
| title_full_unstemmed | Recognizing Non-Collaborative Radio Station Communication Behaviors Using an Ameliorated LeNet |
| title_short | Recognizing Non-Collaborative Radio Station Communication Behaviors Using an Ameliorated LeNet |
| title_sort | recognizing non collaborative radio station communication behaviors using an ameliorated lenet |
| topic | communication behaviors bispectrum estimation signal recognition convolutional neural network (CNN) short-wave radio station |
| url | https://www.mdpi.com/1424-8220/20/15/4320 |
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