Title: Sensitivity Analysis of 3D Building Model-assisted Snapshot Positioning
Author(s): Rakesh Kumar, Mark G. Petovello
Published in: Proceedings of the 29th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2016)
September 12 - 16, 2016
Oregon Convention Center
Portland, Oregon
Pages: 1285 - 1295
Cite this article: Kumar, Rakesh, Petovello, Mark G., "Sensitivity Analysis of 3D Building Model-assisted Snapshot Positioning," Proceedings of the 29th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2016), Portland, Oregon, September 2016, pp. 1285-1295.
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Abstract: Global Navigation Satellite Systems (GNSS) has proven to be a viable and reliable solution in interference-free environment. However, in urban canyons reliable positioning is difficult to achieve in a cost-effective manner using standalone GNSS, due to multipath problems and Non-Line-of-Sight (NLOS) signals. In this regard, the authors previously proposed a method whereby NLOS signals were used constructively by incorporating the information related to nearby reflectors with the help of a 3D building model (3DBM). Since the proposed 3DBM-assisted algorithm incorporates assistance data with the help of a 3DBM, the accuracy of the final estimated position will also depend on the accuracy of the assistance data and processing parameters. This research investigates the sensitivity of the 3DBMassisted positioning algorithm to various algorithm parameters and is tested using data collected in downtown Calgary, Canada. Results confirm that errors in the 3DBM increase the error of the final position estimate. For given grid resolution, the size of the position grid does not affect the final solution. Increasing the grid resolution yields slightly better position estimates at the cost of much higher processing load. The algorithm is shown to be weakly sensitive to the accuracy of receiver’s time estimate; even with a timing error of 10 s, the final estimated position was unchanged. Finally, the coherent integration time (10 ms vs 100 ms) was shown to have very little impact on position accuracy and demonstrates the capability of the algorithm to provide good positioning accuracy without external data bit aiding even in dense urban environments.