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Volume 46 Issue 4
Apr.  2024
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QI Zisen, ZHANG Zixuan, XU Hua, SHI Yunhao. Frequency-Hopping Network Station Sorting Method Using Radio Polarization Characteristics[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1286-1295. doi: 10.11999/JEIT230315
Citation: QI Zisen, ZHANG Zixuan, XU Hua, SHI Yunhao. Frequency-Hopping Network Station Sorting Method Using Radio Polarization Characteristics[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1286-1295. doi: 10.11999/JEIT230315

Frequency-Hopping Network Station Sorting Method Using Radio Polarization Characteristics

doi: 10.11999/JEIT230315
  • Received Date: 2023-04-26
  • Rev Recd Date: 2023-12-08
  • Available Online: 2023-12-20
  • Publish Date: 2024-04-24
  • The paper proposes a hopping signal sorting method based on radio polarization features to address the problem of poor classification and recognition performance of hopping network stations under the condition of similar characteristics in the “space-time-frequency-energy” domain of user and agile frequency hopping parameters using the existing methods. Radio dual-polarization features are introduced into hopping reconnaissance, fully utilizing the differences in cross-polarization discrimination of each user and achieving accurate hopping network station sorting. To address the issue that the cross-polarization discrimination parameter of users of the same type is susceptible to noise pollution, a dual-channel dual-polarization receiving system is constructed to suppress signal noise and ensure the accuracy of polarization feature extraction. Based on the spectral clustering idea, a soft classification decision of polarization characteristics is made, which further improves the network station selection effect and achieves accurate identification of hopping signals. Simulation experiments show that under a 5 dB signal-to-noise ratio, the proposed algorithm can accurately identify multiple hopping network stations under synchronous orthogonal and non-orthogonal networking modes, with a success rate of over 99% in identification and classification, verifying the effectiveness of the new method.
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