Urban Positioning Accuracy Enhancement Utilizing 3D Buildings Model and Accelerated Ray Tracing Algorithm

Nesreen I. Ziedan

Abstract:	The objective of this paper is to enhance the accuracy of urban positioning using all the available LOS, multipath, and NLOS signals. Three algorithms are presented to achieve this objective. The first algorithm is an accelerated ray tracing technique that first eliminates the 3D surfaces that are invisible with respect to a position, and then analyzes the visible surfaces to predict the existence and path lengths of reflected signals. The ray tracing algorithm is applied on the possible range of positions. The second algorithm is a Markov Chain Monte Carlo (MCMC) based algorithm that applies both the Gibbs sampler and the Metropolis-Hastings technique to analyze the received correlated signals to estimate the delays of reflected signals for all the received signals. The third algorithm is a Van Rossum based technique that measures the discrepancy between the estimated delays and the predicted ones at a range of possible positions, where the position that generates the minimum discrepancy is taken as the estimated position. Experimental tests are conducted at two different areas, with different characteristics. The first area is located at the campus of Zagazig University, Egypt, and the second area is located at the campus of Wuhan University, China. The results indicate the ability of the algorithms to successfully utilize reflected signals to enhance urban positioning accuracy.
Published in:	Proceedings of the 30th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2017) September 25 - 29, 2017 Oregon Convention Center Portland, Oregon
Pages:	3253 - 3268
Cite this article:	Ziedan, Nesreen I., "Urban Positioning Accuracy Enhancement Utilizing 3D Buildings Model and Accelerated Ray Tracing Algorithm," Proceedings of the 30th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2017), Portland, Oregon, September 2017, pp. 3253-3268. https://doi.org/10.33012/2017.15366
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