Maximum-Likelihood Power-Distortion Monitoring for GNSS-Signal Authentication

We propose an extension to the so-called power-distortion (PD) detector. The PD detector jointly monitors received power and correlation-profile distortion to detect the presence of global navigation satellite system (GNSS) carryoff-type spoofing, jamming, or multipath. We show that classification performance can be significantly improved by replacing the PD detector's symmetric-difference (SD) based distortion measurement with one based on the postfit residuals of the maximum-likelihood estimate of a single-signal correlation function model. We call the improved technique the power-distortion maximum-likelihood (PD-ML) detector. In direct comparison with the PD detector, the PD-ML detector exhibits improved classification accuracy when tested against an extensive library of recorded field data. In particular, it is 1) significantly more accurate at distinguishing a spoofing attack from a jamming attack; 2) better at distinguishing multipath-afflicted data from interference-free data; and 3) less likely to issue a false alarm by classifying multipath as spoofing. The PD-ML detector achieves this improved performance at the expense of additional computational complexity.