Maximum-Likelihood Power-Distortion Monitoring for GNSS-Signal Authentication
By: Researcher
Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).
Please contact us in case of a broken link from here
| Authors | Jason N. Gross, Cagri Kilic, T. Humphreys |
| Journal/Conference Name | IEEE Transactions on Aerospace and Electronic Systems |
| Paper Category | Aerospace Engineering |
| Paper Abstract | 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. |
| Date of publication | 2019 |
| Code Programming Language | Matlab |
| Comment |
Copyright Researcher 2021