People nowadays spend a tremendous amount of time in indoor structures, causing them to be known as the “indoor generation.” For example, Americans spend, on average, 90% of their time indoors. In light of this, the demand for accurate indoor navigation and localization systems has been more than ever before. Not only accurate indoor navigation enables emerging applications, e.g., location-based services (LBS), it is vital for public safety, e.g., first responders. Numerous competing approaches have been proposed over the past couple of decades for indoor navigation and localization; however, there is no single technology that has emerged as a clear winner in solving this problem.

Among all approaches, cellular long-term evolution (LTE)-based approaches are particularly attractive as they are infrastructure-free, and if properly exploited, can lead to a practical, affordable, and accurate localization system. This is due to the inherent desirable characteristics of LTE signals: abundance, geometric diversity, high bandwidth (up to 20 MHz), high received power (carrier-to-noise ratio (CNR) ranges between 50 and 80 dB-Hz in different indoor conditions), and some of their downlink signals are free to use. Exploiting LTE-based signals for indoor localization comes with several challenges: (i) specialized receivers to opportunistically extract navigation observables from received LTE signals must be designed, and (ii) the clock biases of LTE base stations (also known as evolved Node Bs or eNodeBs) must be removed or estimated, and (iii) errors resulting from short-delay multipath must be mitigated. This Thesis addresses the aforementioned challenges. The foundational contributions of this thesis are demonstrated in showing meter-level accurate indoor navigation.

Global Navigation Satellite Systems (GNSSs) have long been the cornerstone for positioning, navigation, and timing. Despite their widespread use, GNSS signals face vulnerabilities such as jamming, spoofing, and unreliable coverage in various environments like urban canyons, indoors, tunnels, and parking structures. These limitations make exclusive reliance on GNSS inadequate for the rigorous demands of future applications, including autonomous vehicles (AVs), intelligent transportation systems, and location-based services.

To enhance GNSS performance in challenging settings, traditional methods have typically incorporated dead-reckoning sensors like inertial measurement units, lidars, or cameras. These sensors, however, accumulate errors over time and only offer navigation solutions within a local frame, relative to the user equipment’s (UE) initial position. In contrast, alternative signal-based approaches, known as signals of opportunity (SOPs) – encompassing AM/FM radio, satellite communication signals, digital television signals, Wi-Fi, and cellular – hold considerable promise as global navigation sources in GNSS-challenged environments. Among SOPs, cellular signals, particularly from third-generation (3G, code-division multiple access (CDMA)), fourth-generation (4G, long-term evolution (LTE)), and fifth-generation (5G, new radio (NR)) networks, stand out as potential navigation aids. Their navigation-friendly characteristics include ubiquity, geometric diversity, high carrier frequencies, spectral diversity, spatial diversity, broad bandwidth, strong signal strength, and free accessibility.

Nevertheless, as SOPs are primarily designed for communication rather than navigation, utilizing cellular signals for navigational purposes presents several challenges. These include (1) the lack of specific low-level signal and error models for optimal state and parameter extraction for positioning and timing, (2) the absence of published robust, efficient, and reliable receiver architectures to generate navigation observables, (3) continual updates and changes in cellular protocols, and (4) the scarcity of frameworks for high-accuracy navigation using such signals.

This dissertation addresses these challenges, focusing on cellular signals from 4G and 5G networks, with potential extensions to future cellular systems. The foundational contributions of this work are empirically validated on various platforms including ground vehicles (GVs), unmanned aerial vehicles (UAVs), and high-altitude aircraft, demonstrating GNSS-level navigation accuracy.