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  • Signal Acquisition and Tracking ofChirp-Style GPS Jammers

    Ryan H. Mitch, Mark L. Psiaki, Steven P. Powell and Brady W. OHanlon,Cornell University, Ithaca, NY

    BIOGRAPHIES

    Ryan H. Mitch is a Ph.D. candidate in the SibleySchool of Mechanical and Aerospace Engineering atCornell University. He received his B.S. in MechanicalEngineering from the University of Pittsburgh and hisM.S. in Mechanical Engineering from Cornell Univer-sity. His current research interests are in the areas ofGNSS technologies and integrity, nonlinear estimationand filtering, and signal processing.

    Mark L. Psiaki is a Professor in the Sibley School ofMechanical and Aerospace Engineering. He received aB.A. in Physics and M.A. and Ph.D. degrees in Me-chanical and Aerospace Engineering from PrincetonUniversity. His research interests are in the areas ofGNSS technology, applications, and integrity, space-craft attitude and orbit determination, and generalestimation, filtering, and detection.

    Steven P. Powell is a Senior Engineer with the GPSand Ionospheric Studies Research Group in the De-partment of Electrical and Computer Engineering atCornell University. He has M.S. and B.S. degrees inElectrical Engineering from Cornell University. He hasbeen involved with the design, fabrication, testing, andlaunch activities of many scientific experiments thathave flown on high altitude balloons, sounding rock-ets, and small satellites. He has designed ground-basedand space-based custom GPS receiving systems pri-marily for scientific applications.

    Brady W. OHanlon is a Ph.D. candidate in the Schoolof Electrical and Computer Engineering at CornellUniversity. He received both his M.S. and B.S. in Elec-trical and Computer Engineering from Cornell Univer-sity. His interests are in the areas of GNSS technologyand applications, GNSS security, and GNSS as a toolfor space weather research.

    ABSTRACT

    This paper investigates one method of acquiring andone method of tracking various chirp-style GPS jam-mers. A signal model that uses polynomial descrip-tions of frequency versus time is developed. The de-veloped model can track ideal linear chirps as well asmore complicated, and even some non-repeating, poly-nomial frequency patterns. The developed model canalso be used for jammer classification. Two slightlydifferent measurement models that both make use ofFast Fourier Transforms (FFTs) are also developed.An ad-hoc chirp-style signal acquisition method thatuses FFTs of a moderately strong jamming signal isalso developed. The jamming signal model, observa-tion model, and acquisition procedure are combinedto create a chirp-style signal tracking Kalman filter.The developed Kalman filter is verified by applicationon three sets of laboratory data and on two sets offield data. Tracking of a chirp-style jammer is demon-strated for a distance of approximately 1.8 kilometersbetween the receiving station and the jammer. Thejammer signal models and the Kalman filter are alsoexpanded to the scenario where multiple jamming sig-nals are present, and the modified Kalman filter is eval-uated on one set of laboratory data.

    INTRODUCTION

    The Global Positioning Systems location and time-synchronization capabilities are used in many areasof civilian life. Some common civilian uses includenavigation through GPS-enabled smart phones ordash-mounted navigation units, and geotagging pho-tographs. Common commercial uses include trackingtrucking and shipping [1], aircraft and maritime navi-gation [2], and high precision timing applications [3, 4].

    Copyright 2013 by Ryan H. Mitch, Mark L. Psiaki,Brady W. OHanlon, and Steven P. Powell. All rights reserved.

    Preprint from ION GNSS+ 2013

  • Government agencies, such as the police or FBI, canuse GPS for tracking suspected criminals [5].

    Unfortunately, the interests of some individuals can beserved by interfering with GPS. A simple example isthat of a thief who steals a vehicle that is GPS en-abled and wishes to interfere with GPS so that thevehicle cannot be recovered before dismantling. An-other example is that of an employee in a commercialtrucking corporation who wishes to interfere with theGPS tracking device on his company truck so that hemay run personal errands while being paid to makedeliveries. A less malignant use of GPS interferencewould be that of someone attempting to enforce anenvelope of privacy around their personal vehicle [6].

    In the above examples the GPS interference could beprovided by a civil GPS jammer, also known as a Per-sonal Privacy Device (PPD). This has led to severalincidents, of which the so called Newark Incident isthe most commonly recognized. In the Newark Inci-dent a truck driver with a GPS jammer in his vehicledrove by Newark airport and periodically interferedwith the airports GPS equipment [2]. The truck driveronly wanted to enforce an envelope of privacy aroundhis vehicle and was not intending to interfere with theairport equipment. There was also a less benign in-cident in Great Britain where a group of car thievesused GPS jammers to try to disrupt the geolocationand recovery efforts of the relevant authorities [1].

    The above incidents have motivated a number of re-searchers to investigate PPDs [7, 8, 9, 10, 11, 12, 13]in general and their geolocation [14, 15, 16, 17, 18]in specific. This paper furthers the work of [15] andprovides a set of algorithms to acquire and track vari-ous chirp-style GPS jammers. Jammer signal trackinghas applications in jammer geolocation [15], as wouldbe useful for law-enforcement actions. Although, notinvestigated in detail, this work may also have appli-cations in jammer classification and interference miti-gation.

    The remainder of the paper is divided into eight sec-tions. The first section presents background informa-tion on the civilian GPS chirp-style jammers. Thesecond section develops a model and state parameter-ization for the PPDs chirp-style signals. The thirdsection briefly discusses jammer classification usingthe developed models. The fourth section discussesand selects an observation model for use in jammersignal tracking. The fifth section outlines an ad-hocstrategy for acquiring a chirp-style jammer that hasa moderately strong carrier-to-noise ratio. The sixthsection combines the state parameterization, observa-tion model, and acquisition procedure and applies the

    resulting algorithm to data collected from several in-dividual jammers. The seventh section extends thejammer modeling to scenarios where multiple jammersare present and it also considers the new complica-tions that arise when multiple jamming signals mustbe tracked. Additionally, the section applies the multi-jammer tracking algorithm to multi-jammer labora-tory data. The final section summarizes the papersdevelopments and draws appropriate conclusions.

    GPS JAMMER BACKGROUND INFORMA-TION

    Civilian GPS jammers/PPDs can be found in a vari-ety of form factors, but are on average approximatelythe size of a hand-held cellular telephone [8]. Threedifferent civilian GPS jammers are shown in Fig. 1.

    Figure 1 Three different form factors of civilian GPSjammers/PPDs.

    The algorithms used in the processing of signals fromGPS jammers can benefit from an understanding ofthe RF output of those same jammers. The typicaloutput of a civil GPS jammer is shown in Fig. 2. Thehorizontal axis is time and the top plots vertical axis isfrequency. Each vertical slice of the top plot in the fig-ure is a Fast Fourier Transform (FFT) of the RF sam-pled signal, centered at the GPS L1 frequency. Thez, or color axis, is power, with red denoting a largevalue and blue denoting a small value. The bottomplots vertical axis is power. The figure shows a classicexample of a chirp signal, or a tone whose frequency re-peatedly ramps linearly upwards and then resets backto the starting frequency.

    The plot shown in Fig. 2 is a common example of aGPS jammers output spectrum, but other minor vari-ations exist and will be addressed later. Further infor-

    2

  • Figure 2 A common GPS jammer spectrum. The topplot displays vertical slices of 64-point Hamming-win-dowed FFTs, and the bottom plot is of power.

    mation can be found in the survey of civilian GPSjammers in Ref. [8].

    JAMMER MODELING AND PARAMETER-IZATION

    There are multiple parameterizations that could be de-veloped for the chirp-style jammer signal introduced inthe previous section. This paper considers a param-eterization that is similar to that used in Ref. [15],but with several modifications. The parameterizationstarts by assuming a linear chirp, or a first-order rateof change of the frequency versus time, as shown inFig. 3.

    Figure 3 Time-history of the signal frequency of a lin-ear first-order chirp, with appropriate states labeled.

    The following state vector is a moderately low-order

    parameterization of a chirp-style jammer:

    x =

    fu

    d

    Atu

    td

    T

    (1)

    where the entries of the state vector are as follows: isthe phase in units of cycles, f is the frequency in unitsof Hertz, u is the upward frequency rate of changein units of Hertz per second, d is the downward fre-quency rate of change in units of Hertz per second, A isthe amplitude in units of Volts, tu is the ramp up timestart for the current ramp and is in units of seconds,td is the ramp down time start for the current rampand is in units of seconds, T is the chirp period and isin units of seconds. The ramp times could have sepa-rate periods, such as Tu and T d, but experimentationwith separate periods did not dramatically change theresults presented later in this paper.

    The above parameterization assumes that the jammerchirp has a linear first-order polynomial rate of changeof frequency versus time. For the majority of the chirp-style jammers the above parameterization is a suffi-ciently accur