  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.
