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.

中文翻译：

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多模LFM雷达的鲁棒射频指纹识别方案设计

雷达是物联网（IoT）不可或缺的一部分。特定的发射器标识对于标识合法雷达至关重要，更重要的是，拒绝恶意雷达。常规方法依赖于不能识别特定发射器的脉冲参数，因为两个雷达可能具有相同的配置，或者恶意雷达可以执行欺骗攻击。射频指纹（RFF）是由硬件缺陷引起的设备的独特和固有的硬件特性，可以用作设备标识。本文以线性调频（LFM）雷达为例，提出了一种基于RFF的鲁棒可靠的雷达识别方案。该方案首先对脉冲的操作模式进行分类，然后消除噪声影响，最后基于瞬变和基于调制的RFF特征识别雷达发射器。实验结果证明了我们的雷达识别方案在以四个模式运行的三个真实LFM雷达（相同模型）中的有效性，每个模式从每个雷达接收2000个脉冲。当信噪比（SNR）约为5 dB时，四种模式的识别率都高于90％。此外，即使SNR低至-10 dB，模式3仍可实现几乎100％的识别精度。当信噪比（SNR）约为5 dB时，四种模式的识别率都高于90％。此外，即使SNR低至-10 dB，模式3仍可实现几乎100％的识别精度。当信噪比（SNR）约为5 dB时，四种模式的识别率都高于90％。此外，即使SNR低至-10 dB，模式3仍可实现几乎100％的识别精度。