Resilient Navigation through Jamming Detection and Measurement Error Modeling

Global Navigation Satellite Systems (GNSS) provide critical positioning, navigation, and timing (PNT) services across various sectors. GNSS signals are weak when they reach Earth from Medium Earth Orbit (MEO), making them vulnerable to jamming. The jamming threat has been growing over the past decade, putting critical services at risk. In response, the National Space-Based PNT Advisory Board and the White House advocate for policies and technologies to protect, toughen, and augment GPS for a more resilient PNT. Time-sequential estimation improves navigation accuracy and allows for the augmentation of GNSS with other difficult-to-interfere sensors. Safety-critical navigation applications (e.g., GNSS/INS-based aircraft localization) that use time-sequential estimation require high-integrity measurement error time correlation models to compute estimation error bounds. In response, two new methods to identify high-integrity measurement error time correlation models from experimental data are developed and evaluated in this thesis. As opposed to bounding autocorrelation functions in the time domain and power spectra in the frequency domain, methods proposed in this thesis use bounding of lagged product distributions in the time domain and scaled periodogram distributions in the frequency domain. The proposed methods can identify tight-bounding models from empirical data, resulting in tighter estimation error bounds. The sample distributions are bound using theoretical First-order Gauss-Markov process (FOGMP) model distributions derived in this thesis. FOGMP models provide means to account for error time correlation while being easily incorporated into linear estimators. The two methods were evaluated using simulated and experimental GPS measurement error data collected in a mild multipath environment. To protect and alert GNSS end users of jamming, this thesis proposes and evaluates an autonomous algorithm to detect jamming using publicly available data from large receiver networks. The algorithm uses carrier-to-noise ratio (C/N0)-based jamming detectors that are optimal, self-calibrating, receiver-independent, and while adhering to a predefined false alert rate. This algorithm was tested using data from networks with hundreds of receivers, revealing patterns indicative of intentional interference, which provided an opportunity to validate the detector. This validation activity, described in this thesis, consists of designing a portable hardware setup, deriving an optimal power-based jamming monitor for independent detection, and time-frequency analysis of wideband RF (WBRF) data collected during jamming events. The analysis of the WBRF data from a genuine jamming event detected while driving on I-25 in Denver, Colorado, USA, revealed power variations resembling a personal privacy device (PPD), validating the C/N0 detector's result. Finally, this thesis investigates the cause of recurring false alerts in our power-based jamming detectors. These false alerts are caused by a few short pulses of power increases, which other researchers also observe. The time-frequency analysis of signals from the pulses revealed binary data encoded using frequency shift keying (FSK) in the GPS L1 band. Various experiments confirmed the signals are not aliases of out-of-band signals. A survey of similar encoded messages identified the source as car key fobs and other devices transmitting at 315 MHz, nowhere near the GPS L1 band, with an unattenuated 5^th harmonic in the GPS L1 band. The RF emission regulations were analyzed to identify mitigation.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Aerospace Engineering

Department dc:contributor.department

Aerospace and Ocean Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2024

Author dc:creator

• Jada, Sandeep Kiran

Chair dc:contributor.committeechair

• Joerger, Mathieu

Committee members dc:contributor.committeemember

• Woolsey, Craig A.
• Langel, Steven
• Psiaki, Mark L.

• Resilient PNT
• Time-Correlation Modeling
• GNSS Jamming
• Hypothesis Testing

Statement dc:rights

• Creative Commons Attribution 4.0 International

Licence dc:rights.uri

• http://creativecommons.org/licenses/by/4.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:41612

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/121506