Fast and Reliable GNSS Attitude Estimation Using a Constrained Bayesian Ambiguity Resolution Technique (C-BART)

Javier G. Garcia, Penina Axelrad, Pedro A. Roncagliolo, Carlos H. Muravchik

Peer Reviewed

Abstract:	This work presents a new ambiguity resolution method intended for GNSS Attitude Estimation (AE). It is based in a Bayesian approach and incorporates constraints arising from the attitude estimation problem. The new method is shown to be useful not only for the estimation stage but also for validating the estimates with a predefined success rate through the selection of an internal threshold. Special attention is dedicated to the construction of the set of potential ambiguity candidates (the search space), two different approaches are considered, the Global Search Space (GSS) as well as the Local Search Space (LSS). Simulation and experimental results show that fast and reliable ambiguity resolution can be achieved using only single frequency phase measurements. This significantly increases the GNSS based attitude solution availability and notably reduces the time required to the first ambiguity solution. This is critical for some applications where power is a limiting factor and a continuous operation is not possible, or when there are obstructions that prevent the continuous tracking of a sufficient number of satellites to provide an attitude solution.
Published in:	Proceedings of the 28th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2015) September 14 - 18, 2015 Tampa Convention Center Tampa, Florida
Pages:	2809 - 2820
Cite this article:	Garcia, Javier G., Axelrad, Penina, Roncagliolo, Pedro A., Muravchik, Carlos H., "Fast and Reliable GNSS Attitude Estimation Using a Constrained Bayesian Ambiguity Resolution Technique (C-BART)," Proceedings of the 28th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2015), Tampa, Florida, September 2015, pp. 2809-2820.
Full Paper:	ION Members/Non-Members: 1 Download Credit Sign In