Time-Frequency Analysis of GNSS Jamming Events Detected on U.S. Highways

Sandeep Jada, John Bowman, Mark Psiaki, Chenming Fan, Mathieu Joerger

Abstract: In this paper, we implement jamming detectors designed for off-the-shelf GNSS receivers using publicly available data collected at more than 900 receiver locations during an eight-month-long period. We identify spatial and temporal patterns in the detected events to predict when and where jamming may occur. We find patterns that coincide with daily driver commutes and weekly delivery schedules along U.S. highways. We then validate this approach by developing a new Neyaman-Pearson locally-optimal signal power monitor using wideband radio-frequency (RF) data, and by deploying our own equipment at the locations and times of the predicted jamming. Two example wideband data sets are presented, which we collected in Colorado and Virginia. We analyze this data in the time-frequency domain and show interference in the GPS L1 band caused by recurring unidentified communication broadcasts and by personal privacy devices (PPDs).
Published in: Proceedings of the 35th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2022)
September 19 - 23, 2022
Hyatt Regency Denver
Denver, Colorado
Pages: 933 - 946
Cite this article: Jada, Sandeep, Bowman, John, Psiaki, Mark, Fan, Chenming, Joerger, Mathieu, "Time-Frequency Analysis of GNSS Jamming Events Detected on U.S. Highways," Proceedings of the 35th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2022), Denver, Colorado, September 2022, pp. 933-946. https://doi.org/10.33012/2022.18528
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