Frequency hopping modulation recognition based on time-frequency energy spectrum texture feature
For frequency hopping modulation identification,a novel method based on time-frequency energy spectrum texture feature was proposed.Firstly,the time-frequency diagram of the frequency hopping signal was obtained by smoothed pseudo Wigner-Ville distribution,and the background noise of the time-freque...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2019-10-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019191/ |
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| _version_ | 1841539334212157440 |
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| author | Hongguang LI Ying GUO Ping SUI Zisen QI |
| author_facet | Hongguang LI Ying GUO Ping SUI Zisen QI |
| author_sort | Hongguang LI |
| collection | DOAJ |
| description | For frequency hopping modulation identification,a novel method based on time-frequency energy spectrum texture feature was proposed.Firstly,the time-frequency diagram of the frequency hopping signal was obtained by smoothed pseudo Wigner-Ville distribution,and the background noise of the time-frequency diagram was removed by two-dimensional Wiener filtering to improve the resolution of the time-frequency diagram under low SNR conditions.Then,the connected-domain detection algorithm was used to extract the time-frequency energy spectrum of each hop signal and convert it into a time-frequency gray-scale image.The histogram statistical features and the gray-scale co-occurrence matrix feature were combined to form a 22-dimensional eigenvector.Finally,the feature set was trained,classified and identified by optimized support vector machine classifier.Simulation experiments show that the multi-dimensional feature vector extracted by the algorithm has strong representation ability and avoids the misjudgment caused by the similarity of single features.The average recognition accuracy of the six modulation methods of frequency hopping signals BPSK,QPSK,SDPSK,QASK,64QAM and GMSK is 91.4% under the condition of -4 dB SNR. |
| format | Article |
| id | doaj-art-2c056083cb264655b9542f59d7de6ed6 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2019-10-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-2c056083cb264655b9542f59d7de6ed62025-01-14T07:17:51ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2019-10-0140202959730018Frequency hopping modulation recognition based on time-frequency energy spectrum texture featureHongguang LIYing GUOPing SUIZisen QIFor frequency hopping modulation identification,a novel method based on time-frequency energy spectrum texture feature was proposed.Firstly,the time-frequency diagram of the frequency hopping signal was obtained by smoothed pseudo Wigner-Ville distribution,and the background noise of the time-frequency diagram was removed by two-dimensional Wiener filtering to improve the resolution of the time-frequency diagram under low SNR conditions.Then,the connected-domain detection algorithm was used to extract the time-frequency energy spectrum of each hop signal and convert it into a time-frequency gray-scale image.The histogram statistical features and the gray-scale co-occurrence matrix feature were combined to form a 22-dimensional eigenvector.Finally,the feature set was trained,classified and identified by optimized support vector machine classifier.Simulation experiments show that the multi-dimensional feature vector extracted by the algorithm has strong representation ability and avoids the misjudgment caused by the similarity of single features.The average recognition accuracy of the six modulation methods of frequency hopping signals BPSK,QPSK,SDPSK,QASK,64QAM and GMSK is 91.4% under the condition of -4 dB SNR.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019191/frequency hopping modulation recognitiontime-frequency energy spectrumtime-frequency gray scale imagehistogram statisticsgray co-occurrence matrix |
| spellingShingle | Hongguang LI Ying GUO Ping SUI Zisen QI Frequency hopping modulation recognition based on time-frequency energy spectrum texture feature Tongxin xuebao frequency hopping modulation recognition time-frequency energy spectrum time-frequency gray scale image histogram statistics gray co-occurrence matrix |
| title | Frequency hopping modulation recognition based on time-frequency energy spectrum texture feature |
| title_full | Frequency hopping modulation recognition based on time-frequency energy spectrum texture feature |
| title_fullStr | Frequency hopping modulation recognition based on time-frequency energy spectrum texture feature |
| title_full_unstemmed | Frequency hopping modulation recognition based on time-frequency energy spectrum texture feature |
| title_short | Frequency hopping modulation recognition based on time-frequency energy spectrum texture feature |
| title_sort | frequency hopping modulation recognition based on time frequency energy spectrum texture feature |
| topic | frequency hopping modulation recognition time-frequency energy spectrum time-frequency gray scale image histogram statistics gray co-occurrence matrix |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019191/ |
| work_keys_str_mv | AT hongguangli frequencyhoppingmodulationrecognitionbasedontimefrequencyenergyspectrumtexturefeature AT yingguo frequencyhoppingmodulationrecognitionbasedontimefrequencyenergyspectrumtexturefeature AT pingsui frequencyhoppingmodulationrecognitionbasedontimefrequencyenergyspectrumtexturefeature AT zisenqi frequencyhoppingmodulationrecognitionbasedontimefrequencyenergyspectrumtexturefeature |