For outdoor navigation, GPS provides the most widely-used means of node localization; however, the level of accuracy provided by low-cost receivers is typically insufficient for use in high-precision applications such as land surveying, vehicle collision avoidance, and formation flying, among others. Additionally, these applications do not require precise absolute Earth coordinates, but rather rely on relative positioning to infer information about the geometric configuration of the constituent nodes in a system. This dissertation presents a novel approach that uses GPS to derive relative location information for a scalable network of single-frequency receivers. Nodes in a network share their raw satellite measurements and track the positions of neighboring nodes as opposed to computing their own absolute coordinates. Random and systematic errors are mitigated in novel ways, challenging long-standing beliefs that precision GPS systems require extensive
stationary calibration times or complex equipment configurations. In addition to the mathematical basis for our technique, a working prototype is developed using a network of mobile devices with custom Bluetooth-enabled GPS sensors, enabling experimental evaluation of several real-world test scenarios. Our evaluation shows that sub-meter relative positioning accuracy at an update rate of 1 Hz is possible under various conditions with the presented technique. This is an order of magnitude more accurate than simply taking the difference of standalone receiver coordinates or other simplistic approaches.