RF Signal Feature Extraction in Integrated Sensing and Communication
Because of the open property of information sharing in integrated sensing and communication, it is inevitable to face security problems such as user information being tampered, eavesdropped, and copied. Radio frequency (RF) individual identification technology is an important means to solve its secu...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi-IET
2023-01-01
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| Series: | IET Signal Processing |
| Online Access: | http://dx.doi.org/10.1049/2023/4251265 |
| _version_ | 1827008587551997952 |
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| author | Xiaoya Wang Songlin Sun Haiying Zhang Qiang Liu |
| author_facet | Xiaoya Wang Songlin Sun Haiying Zhang Qiang Liu |
| author_sort | Xiaoya Wang |
| collection | DOAJ |
| description | Because of the open property of information sharing in integrated sensing and communication, it is inevitable to face security problems such as user information being tampered, eavesdropped, and copied. Radio frequency (RF) individual identification technology is an important means to solve its security problems at present. Whether using machine learning methods or current deep learning-based target fingerprint identification, its performance is based on how well the radio frequency features (RFF) are extracted. Since the received signal is affected by various factors, we believe that we should first find the intrinsic features that can describe the properties of the target, which is the key to enhance the RF fingerprint recognition. In this paper, we try to analyze the intrinsic characteristics of the components that influenced the signal by the transmitting source and derive a mathematical formula to describe the RF characteristics. We propose a method using dynamic wavelet transform and wavelet spectrum (DWTWS) to enhance RFF features. The performance of the proposed method was evaluated by experimental data. Using a support vector machine classifier, the recognition accuracy is 99.6% for 10 individuals at a signal-to-noise ratio (SNR) of 10 dB. In comparison with the dual-tree complex wavelet transform (DT-CWT) feature extraction method and the wavelet scattering transform method, the DWTWS method has increased the interclass distance of different individuals and enhanced the recognition accuracy. The DWTWS method is superior at low SNR, with performance improvements of 53.1% and 10.7% at 0 dB. |
| first_indexed | 2024-03-09T06:46:14Z |
| format | Article |
| id | doaj.art-50bab4927b9342eabec2dc30a58059d8 |
| institution | Directory Open Access Journal |
| issn | 1751-9683 |
| language | English |
| last_indexed | 2025-02-18T12:36:11Z |
| publishDate | 2023-01-01 |
| publisher | Hindawi-IET |
| record_format | Article |
| series | IET Signal Processing |
| spelling | doaj.art-50bab4927b9342eabec2dc30a58059d82024-11-02T04:14:00ZengHindawi-IETIET Signal Processing1751-96832023-01-01202310.1049/2023/4251265RF Signal Feature Extraction in Integrated Sensing and CommunicationXiaoya Wang0Songlin Sun1Haiying Zhang2Qiang Liu3School of Information and Communication EngineeringSchool of Information and Communication EngineeringCETC 54th Research InstituteCollege of Electronic and Information EngineeringBecause of the open property of information sharing in integrated sensing and communication, it is inevitable to face security problems such as user information being tampered, eavesdropped, and copied. Radio frequency (RF) individual identification technology is an important means to solve its security problems at present. Whether using machine learning methods or current deep learning-based target fingerprint identification, its performance is based on how well the radio frequency features (RFF) are extracted. Since the received signal is affected by various factors, we believe that we should first find the intrinsic features that can describe the properties of the target, which is the key to enhance the RF fingerprint recognition. In this paper, we try to analyze the intrinsic characteristics of the components that influenced the signal by the transmitting source and derive a mathematical formula to describe the RF characteristics. We propose a method using dynamic wavelet transform and wavelet spectrum (DWTWS) to enhance RFF features. The performance of the proposed method was evaluated by experimental data. Using a support vector machine classifier, the recognition accuracy is 99.6% for 10 individuals at a signal-to-noise ratio (SNR) of 10 dB. In comparison with the dual-tree complex wavelet transform (DT-CWT) feature extraction method and the wavelet scattering transform method, the DWTWS method has increased the interclass distance of different individuals and enhanced the recognition accuracy. The DWTWS method is superior at low SNR, with performance improvements of 53.1% and 10.7% at 0 dB.http://dx.doi.org/10.1049/2023/4251265 |
| spellingShingle | Xiaoya Wang Songlin Sun Haiying Zhang Qiang Liu RF Signal Feature Extraction in Integrated Sensing and Communication IET Signal Processing |
| title | RF Signal Feature Extraction in Integrated Sensing and Communication |
| title_full | RF Signal Feature Extraction in Integrated Sensing and Communication |
| title_fullStr | RF Signal Feature Extraction in Integrated Sensing and Communication |
| title_full_unstemmed | RF Signal Feature Extraction in Integrated Sensing and Communication |
| title_short | RF Signal Feature Extraction in Integrated Sensing and Communication |
| title_sort | rf signal feature extraction in integrated sensing and communication |
| url | http://dx.doi.org/10.1049/2023/4251265 |
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