GNSS Spoofing, Jamming, and Multipath Interference Classification using a Maximum-Likelihood Multi-Tap Multipath Estimator

Jason N. Gross and Todd E. Humphreys

Peer Reviewed

Abstract: This paper experimentally evaluates the application of existing multipath mitigation technology in conjunction with in-band power monitoring for the purpose of GNSS interference classification. In particular, GNSS interference detection and classification metrics derived from the output of a multiple-correlation tap, maximum-likelihood multipath estimator are jointly used for the alarming the presence of GNSS spoofing, jamming or multipath. This approach is evaluated against the Texas Spoofing Text Battery (TEXBAT) archive (Humphreys et al., 2012), a dozen sets of deep urban multipath recordings, several recordings of wideband jammers at several different power levels, and clean static data recordings. Two detection approaches are proposed and one is shown to be better at discriminating between spoofing and jamming attacks.
Published in: Proceedings of the 2017 International Technical Meeting of The Institute of Navigation
January 30 - 2, 2017
Hyatt Regency Monterey
Monterey, California
Pages: 662 - 670
Cite this article: Gross, Jason N., Humphreys, Todd E., "GNSS Spoofing, Jamming, and Multipath Interference Classification using a Maximum-Likelihood Multi-Tap Multipath Estimator," Proceedings of the 2017 International Technical Meeting of The Institute of Navigation, Monterey, California, January 2017, pp. 662-670. https://doi.org/10.33012/2017.14919
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