A GPS spoofing detection and classification correlator-based technique using the LASSO

Abstract: This work 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 (TEXBAT). The proposed method achieves 0.3% detection error rate (DER) for a spoofer attack in nominal signal-to-noise ratio (SNR) conditions for an authentic-over-spoofer power of 3 dB.