  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.
