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. / Doctor of Philosophy / Global Navigation Satellite Systems (GNSS) have become integral to modern-day life. Many essential services rely on GNSS-provided Positioning, Navigation, and Timing (PNT) services; power grids rely on accurate GNSS-provides timing for synchronization; stock markets use them for time-stamping trades; aircraft and ships use GNSS to correct accumulated position errors regularly; to name a few. In addition, the availability of cheap and accessible PNT services combined with mobile internet spawned new service sectors through mobile applications. A 2019 study published by the National Institute of Standards and Technology (NIST) estimates that GPS has generated $1.4 trillion in U.S. economic benefits since the system became available in the 1980s. With the wide adoption of GNSS services comes new motives for interference. These motives can range from delivery workers and truck drivers trying to hide their location from their employers to something more nefarious, such as criminals trying to evade law enforcement surveillance. GNSS jamming is a type of interference in which the attacker drowns out the faint GNSS signals, broadcast from medium Earth Orbit (MEO) at 20,000 km, with a powerful RF transmitter. Some commonly used devices are transmitters are cheaply available for as low as $10 on Amazon, known as personal privacy devices (PPDs). Another source of jamming comes from militaries in conflict zones overseas, jamming GNSS signals over large areas of a country or a city. However, two major incidents in the US have disrupted air traffic over busy airspace, such as in Denver and Dallas. This threat of GNSS interference has grown over the past decade and is only getting worse. The White House and other organizations advocate for policies for a more resilient PNT; to protect, toughen, and augment GNSS. % This thesis contributes to protecting GNSS frequencies through autonomous algorithms that process publicly available signal quality data from large receiver networks for jamming detection. This autonomous algorithm uses detectors that are self-calibrating and optimal, i.e., minimizing the probability of missed detection while targeting a predefined false alert probability. Several jamming event patterns consistent with intentional interference were detected using this algorithm. The signal-quality-based detectors were validated using an independent power-based optimal jamming detector derived in this thesis. Spurious recurring false alerts triggered the power detector. An investigation described in the thesis discovered that car key fobs and other devices emit RF energy in restricted GPS frequencies. Based on the analysis of FCC regulation for RF transmitters, mitigation is proposed for power-based jamming detectors to prevent false alarms. Time-sequential estimation improves navigation accuracy and allows for the augmentation of GNSS with other difficult-to-interfered sensors such as IMU or LIDAR. Safety-critical navigation applications can benefit from time-sequential estimation, but they require high-integrity measurement error time correlation models to compute bounds on positioning errors. Two new methods to derive high-integrity measurement error time correlation models from experimental data are developed and evaluated in this thesis. These methods can derive tighter bounding models compared to the existing methods, reducing the uncertainty in position estimates. The two methods were implemented and evaluated using simulated and experimental GPS measurement error data collected in a mild multipath environment.