gps multipath detection and mitigation in urban environments...simplify simulation. the ray-tracing...
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GPS Multipath Detection and Mitigation in Urban EnvironmentsShiwen Zhang, Sherman Lo, Yu-Hsuan Chen, J. David Powell
GPS Laboratory, Department of Aeronautics & Astronautics, Stanford University
M. Obst, S. Bauer and G. Wanielik, "Urban multipath detection and mitigation with dynamic 3D
maps for reliable land vehicle localization," Proceedings of the 2012 IEEE/ION Position,
Location and Navigation Symposium, Myrtle Beach, SC, 2012, pp. 685-691.
GPS multipath refers to the phenomenon when a satellite signal is
received off a reflecting surface (such as buildings) rather than
unimpeded from the satellite. Such reflections can cause significant
errors in the user navigation solutions. Multipath is particularly significant
and common in urban environment. Identifying and reducing the effect of
multipath would enable GPS and the other satellite navigation
constellations to contribute to high integrity railway control and
autonomous vehicle operating in urban environments.
Introduction
The purpose of this study is to detect and mitigate GPS multipath error
to improve position accuracy and trustworthiness in urban environment.
Purpose
This study examines a multipath detection algorithm followed by satellite
exclusion. The proposed algorithm uses a ray-tracing algorithm on a 3D
building model to predict the presence of both LOS and reflected signals
given a satellite-user geometry. A statistical analysis was then performed
to help quantify the confidence level of the building model’s multipath
prediction under modeling uncertainty. Experimental data were collected
to evaluate the performance of the algorithm.
Methods
GPS data were collected at 13 ground locations on the Engineering
Quad using a NovAtel receiver. Four different algorithms were applied at
each location. The no exclusion algorithm uses all received signals to
calculate position solution. The residual checking algorithm excludes
outlier signals one by one until the residuals are consistent with each
other. The hard exclusion algorithm excludes signals based on the
building model’s predictions of LOS and reflection. The soft exclusion
algorithm performs satellite exclusion based on the confidence level of
the model’s prediction . The hard and soft exclusion may be explained
more clearly – you may do that in your talk rather than in the text
Results
This study examined a multipath detection method that uses 3D building
model. The soft exclusion algorithm reduced multipath effects on
position error from as large as 17 meters to 1.2 meters and achieved an
average position error of 1.6 meters. Experimental results show the
need for a reasonable initial user position estimate.
Conclusions
Bibliography
The authors would like to thank the Center for Automotive Research at
Stanford (CARS) and the Stanford Center for Position Navigation and
Time (SCPNT) for their support in this research.
Acknowledgments
The author can be reached at [email protected]
A PDF version of this poster can be found at
https://stanford.box.com/s/utl800pop1hllfvzrjlytiubgicplyn0
Further Information
More LogosAbstract #
20980123
Line of sight coverage Multipath range errorNumber of reflection
30 m
10 m
15 m
60-deg
elevation
LOS
Reflection
A 3D building model was developed for ray-tracing simulation and multipath prediction. The site
chosen for simulation and testing is the Engineering Quad at Stanford University. The building
model was constructed using building corner coordinates and building heights estimated from
Google Earth. Detail structure of the building walls and roofs were not captured in the model to
simplify simulation. The ray-tracing algorithm simulates the LOS signal path as well as all the
building-reflected signal paths from the satellite to the user receiver.
Modeling and Simulation