Maximum-Likelihood Power-Distortion Monitoring for GNSS Signal Authentication

TL;DR

This paper introduces the PD-ML detector, an enhanced GNSS signal authentication method that improves classification accuracy of spoofing, jamming, and multipath by using maximum-likelihood residuals, despite increased computational demands.

Contribution

The paper presents the PD-ML detector, a novel extension of the PD detector that leverages maximum-likelihood residuals for better classification of GNSS signal threats.

Findings

Significantly more accurate at distinguishing spoofing from jamming.

Better at differentiating multipath from interference.

Less prone to false alarms classifying multipath as spoofing.

Abstract

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.