Exploring GPS Signal Data and Position Estimation Through Bayesian Inference

Abstract

A new approach to position determination using the Global Positioning System (GPS) has been developed where post processing of ultra-short sequences of captured GPS satellite signal data can produce an estimate of receiver location. The new fixing scheme, labelled `FastFix' works by downloading GPS satellite information (ephemeris) data from the internet rather than decoding the same data (at a rate of 50bps) from each satellite's broadcast signal. Fewer than five milliseconds of signal data, sampled at around 8MHz, is processed in a similar manner to a traditional GPS receiver chip (C/A code phases and carrier Doppler shifts are found through standard signal processing techniques) before a least squares optimization is used to estimate receiver position and GPS time. This thesis builds on the FastFix scheme by analyzing the binary signal data (output from the GPS receiver chip) using Bayesian inference with Markov chain Monte Carlo sampling. The approach presented in this thesis borrows ideas from the FastFix scheme to begin the position determination process and results in position estimates typically within 250 meters of true receiver location. The Bayesian approach allows not only position and time estimates but also their relative uncertainty to be determined.