Leila Taleghani, Fabian Rothmaier, Yu-Hsuan Chen, Sherman Lo, Todd Walter, Stanford University; Dennis Akos, Benon Granite Gattis, University of Colorado

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Radio Frequency Interference (RFI) is an ever-increasing concern for the use of the Global Navigation Satellite System (GNSS). These signals can interfere with a user’s ability to receive the desired GNSS signals (jamming) or even worse, replace them with simulated signals that cause the user to obtain the wrong position and/or time estimate (spoofing). Such interfering signals may be fairly weak and only effective in a fairly limited area. Many potential schemes have been proposed for interference detection, including on board detection/rejection, crowdsourced methods and dedicated networks of sensors. We believe that we have identified a potential gap-filler that can enhance the detection of RFI, particularly when used in conjunction with the aforementioned methods. We are developing a low cost, distributed RFI detector based on a commercial off-the-shelf GNSS receiver, the u-blox F9. This receiver can monitor two different RF bands and many core-constellation signals. Our work seeks to leverage inexpensive commercial receivers with dual bands and hundreds of channels to develop a low-cost sensor to blanket a local region that needs protection, and economically detect even very localized interference sources in complex environments. This paper presents an initial set of metrics and thresholds to identify events of interest and define background interference levels. We present results displaying consistency in the receiver’s and satellites’ position solutions under nominal conditions and lack of consistency under the presence of RFI events during a United States government sanctioned jamming and spoofing test. We present power monitoring metrics including automatic gain control (AGC), programmable gain amplifier (PGA), carrier to noise density ratio (C/N0), and spectral analysis that we use to identify and characterize RFI events. Our analysis shows the benefits of continuously monitoring a wide variety of metrics and performing consistency checks.