Xing, Yuexiu, Hu, Aiqun, Zhang, Junqing 
ORCID: 0000-0002-3502-2926, Yu, Jiabao, Li, Guyue and Wang, Ting
(2020)
Design of a Robust Radio-Frequency Fingerprint Identification Scheme for Multimode LFM Radar.
IEEE INTERNET OF THINGS JOURNAL, 7 (10).
pp. 10581-10593.
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IoTJ_RFF_Radar.pdf - Author Accepted Manuscript Download (1MB) | Preview |
Abstract
Radar is an indispensable part of the Internet of Things (IoT). Specific emitter identification is essential to identify the legitimate radars and, more importantly, to reject the malicious radars. Conventional methods rely on pulse parameters that are not capable to identify the specific emitter as two radars may have the same configuration or a malicious radar can perform spoofing attacks. Radio-frequency fingerprint (RFF) is the unique and intrinsic hardware characteristic of devices resulted from hardware imperfection, which can be used as the device identity. This article proposes a robust and reliable radar identification scheme based on the RFF, taking linear frequency modulation (LFM) radar as a case study. This scheme first classifies the operation mode of the pulses, then eliminates the noise effect, and finally identifies the radar emitters based on the transient and modulation-based RFF features. The experimental results verify the effectiveness of our radar identification scheme among three real LFM radars (same model) operating at four modes, each mode with 2000 pulses from each radar. The identification rates of the four modes are all higher than 90% when the signal-to-noise ratio (SNR) is about 5 dB. In addition, mode 3 achieves almost 100% identification accuracy even when the SNR is as low as -10 dB.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Spaceborne radar, Feature extraction, Modulation, Signal to noise ratio, Noise reduction, Hardware, Denoising, linear frequency modulation (LFM), multimode radar, radar identification, radio-frequency fingerprint (RFF) |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 26 Jun 2020 10:42 |
| Last Modified: | 18 Jan 2023 23:48 |
| DOI: | 10.1109/JIOT.2020.3003692 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3090784 |
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