Radio Signal Recognition Using Two-Stage Spatiotemporal Network with Bispectral Analysis
محفوظ في:
| الحاوية / القاعدة: | Sensors vol. 25, no. 17 (2025), p. 5449-5468 |
|---|---|
| المؤلف الرئيسي: | |
| مؤلفون آخرون: | , , |
| منشور في: |
MDPI AG
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| الموضوعات: | |
| الوصول للمادة أونلاين: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| الوسوم: |
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| 024 | 7 | |a 10.3390/s25175449 |2 doi | |
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| 045 | 2 | |b d20250101 |b d20251231 | |
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| 100 | 1 | |a Bai Hongmei |u College of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu 610059, China; 202201130113@cudt.edu.cn (H.B.); jiayong2014@cdut.edu.cn (Y.J.); 202320030123@cudt.edu.cn (B.X.) | |
| 245 | 1 | |a Radio Signal Recognition Using Two-Stage Spatiotemporal Network with Bispectral Analysis | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a With the rapid proliferation of unmanned aerial vehicles (UAVs), reliable identification based on radio frequency (RF) signals has become increasingly important for both civilian and security applications. This paper proposes a spatiotemporal feature extraction and classification framework based on bispectral analysis. Specifically, bispectral estimation is used to convert one-dimensional RF signals into two-dimensional bispectrum feature maps that capture higher-order spectral characteristics and nonlinear dependencies. Based on these characteristics, a two-stage network was constructed for spatiotemporal feature extraction and classification. The first stage utilizes a ResNet18 network to extract spatial structural features from individual bispectrum maps. The second stage employs an LSTM network to learn temporal dependencies across the sequence of bispectrum maps, capturing the continuity and evolution of signal characteristics over time. The experimental results on a public dataset of UAV RF signals show that this method improves recognition accuracy by 6.78% to 13.89% compared to other existing methods across five categories of UAVs. | |
| 653 | |a Unmanned aerial vehicles | ||
| 653 | |a Accuracy | ||
| 653 | |a Methods | ||
| 653 | |a Deep learning | ||
| 653 | |a Wavelet transforms | ||
| 653 | |a Fourier transforms | ||
| 653 | |a Radio frequency | ||
| 653 | |a Identification | ||
| 653 | |a Signal processing | ||
| 653 | |a Classification | ||
| 700 | 1 | |a Li, Siming |u School of Computer and Cyber Security, Chengdu University of Technology, Chengdu 610059, China | |
| 700 | 1 | |a Jia, Yong |u College of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu 610059, China; 202201130113@cudt.edu.cn (H.B.); jiayong2014@cdut.edu.cn (Y.J.); 202320030123@cudt.edu.cn (B.X.) | |
| 700 | 1 | |a Bowen, Xiao |u College of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu 610059, China; 202201130113@cudt.edu.cn (H.B.); jiayong2014@cdut.edu.cn (Y.J.); 202320030123@cudt.edu.cn (B.X.) | |
| 773 | 0 | |t Sensors |g vol. 25, no. 17 (2025), p. 5449-5468 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3249714661/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3249714661/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3249714661/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |