Autonomous vehicles (AVs) will demand a resilient, accurate, and tamper-proof navigation system. Current AV navigation systems will not meet these demands as they are dependent on global navigation satellite system (GNSS) signals, which are jammable, spoofable, and may not be usable in certain environments (e.g., indoors and deep urban canyons). Dead-reckoning sensors (e.g., inertial measurements units, lidars, or cameras) are typically used to aid GNSS signals in challenged environment. However, such sensors accumulate errors with time and can only provide a navigation solution in a local frame, i.e., relative to the AV's initial position. Alternatively, signals of opportunity (SOPs) (e.g., AM/FM radio, low Earth orbit satellite signals, Wi-Fi, and cellular) may be used as a global navigation source in GNSS-challenged environment.

Cellular signals, particularly code-division multiple access (CDMA), long-term evolution (LTE), and fifth generation (5G) new radio (NR) signals, are among the most promising SOP candidates for navigation. These signals are (i) abundant, (ii) received at a much higher power and bandwidth than GNSS signals, (iii) offer a favorable horizontal geometry, (iv) are diverse in the radio frequency spectrum, (v) and are free to use. These inherent attributes make them attractive navigation sources for AVs in GNSS-challenged environments.

However, since SOPs are not intended for navigation, there are several challenges associated with using cellular signals and SOPs in general for navigation: (1) the unavailability of appropriate low-level signal and error models for optimal extraction of states and parameters of interest for positioning and timing purposes, (2) the absence of published receiver architectures capable of producing navigation observables, (3) the unknown fundamental performance bounds, (4) and the lack of frameworks for high accuracy navigation with such signals. This dissertation addresses the aforementioned challenges for cellular SOPs, focusing on CDMA systems, with extensions to LTE and 5G. The foundational contributions of this dissertation are demonstrated on ground vehicles and unmanned aerial vehicles (UAVs), showing meter-level accurate navigation for the former and sub-meter-level accurate navigation for the latter.