This article proposes a global navigation satellite system (GNSS) spoofing detection and classification technique for single-antenna receivers. We formulate an optimization problem at the baseband correlator domain by using the Least Absolute Shrinkage and Selection Operator (LASSO). We model correlator tap outputs of the received signal to form a dictionary of triangle-shaped functions and leverage sparse signal processing to choose a decomposition of shifted matching triangles from said dictionary. The optimal solution of this minimization problem discriminates the presence of a potential spoofing attack peak by observing a decomposition of two different code-phase values (authentic and spoofed) in a sparse vector output. We use a threshold to mitigate false alarms. Furthermore, we present a variation of the minimization problem by enhancing the dictionary to a higher resolution of shifted triangles. The proposed technique can be implemented as an advanced fine-acquisition monitoring tool to aid in the tracking loops for spoofing mitigation. In our experiments, we are able to distinguish authentic and spoofer peaks from synthetic data simulations and from a real dataset, namely, the Texas spoofing test battery. The proposed method achieves 0.3% detection error rate for a spoofer attack in nominal signal-to-noise ratio conditions and an authentic-over-spoofer power of 3 dB.

中文翻译：

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使用 LASSO 的基于 GPS 欺骗检测和分类相关器的技术

本文提出了一种用于单天线接收机的全球导航卫星系统（GNSS）欺骗检测和分类技术。我们通过使用最小绝对收缩和选择算子 (LASSO) 在基带相关器域制定优化问题。我们对接收信号的相关器抽头输出进行建模以形成三角形函数的字典，并利用稀疏信号处理从所述字典中选择移位匹配三角形的分解。此最小化问题的最佳解决方案通过观察稀疏向量输出中两个不同代码相位值（真实和欺骗）的分解来区分潜在欺骗攻击峰值的存在。我们使用阈值来减轻误报。此外，我们通过将字典增强到更高分辨率的移位三角形来呈现最小化问题的变体。所提出的技术可以作为一种先进的精细采集监控工具来实施，以帮助跟踪循环以减轻欺骗。在我们的实验中，我们能够从合成数据模拟和真实数据集（即德克萨斯欺骗测试电池）中区分真实和欺骗峰值。所提出的方法在标称信噪比条件下实现了 0.3% 的欺骗攻击检测错误率和 3 dB 的真实欺骗攻击功率。我们能够从合成数据模拟和真实数据集（即德克萨斯欺骗测试电池）中区分真实和欺骗峰值。所提出的方法在标称信噪比条件下实现了 0.3% 的欺骗攻击检测错误率和 3 dB 的真实欺骗攻击功率。我们能够从合成数据模拟和真实数据集（即德克萨斯欺骗测试电池）中区分真实和欺骗峰值。所提出的方法在标称信噪比条件下实现了 0.3% 的欺骗攻击检测错误率和 3 dB 的真实欺骗攻击功率。