Maximum-Likelihood Power-Distortion Monitoring for GNSS Signal Authentication
This work addresses signal authentication for GNSS systems, offering incremental improvements in detection accuracy for spoofing, jamming, and multipath scenarios.
The authors tackled the problem of detecting GNSS signal spoofing, jamming, and multipath by proposing an improved detector that replaces a symmetric-difference-based distortion measurement with one based on maximum-likelihood residuals, resulting in significantly better classification accuracy in distinguishing attacks and reducing false alarms.
We propose an extension to the so-called PD detector. The PD detector jointly monitors received power and correlation profile distortion to detect the presence of GNSS carry-off-type spoofing, jamming, or multipath. We show that classification performance can be significantly improved by replacing the PD detector's symmetric-difference-based distortion measurement with one based on the post-fit residuals of the maximum-likelihood estimate of a single-signal correlation function model. We call the improved technique the 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.