Accuracy assessment of GPS navigation augmented by SAR and LiDAR-derived Digital Elevation Models

Remotely sensed Digital Elevation Models (DEM) can be used to augment a standalone Global Positioning System (GPS) by adding an extra range observation which measures the distance to the Earth centre. This method so called height aiding can reduce the number of GPS satellites required to get a 3D position fix from four to three and hence improve the performance of the GPS navigation algorithm in terms of accuracy, reliability and availability. Up until now, the accuracy of height aided GPS navigation using higher resolution Synthetic Aperture Radar (SAR) and Light Detection and Ranging (LiDAR)-derived elevation data has not been fully evaluated in a broad spectrum of navigation scenarios. This article provides a robust and accurate analysis on how much range error is introduced by height aiding using 5 m spacing SAR and 1 m spacing LiDAR-derived DEMs under in-car and personal navigation situations. Based on the experimental results obtained from both dynamic and static tests, suggestions have been made on what level of vertical and positional accuracy can be achieved as well as the related DEM quality issues for navigation purposes.

[1]  Jing Li,et al.  Accuracy and reliability of map-matched GPS coordinates: the dependence on terrain model resolution and interpolation algorithm , 2005, Comput. Geosci..

[2]  Geoffrey Blewitt,et al.  Basics of the GPS Technique : Observation Equations , 2000 .

[3]  Dah-Jing Jwo,et al.  Efficient DOP Calculation for GPS with and without Altimeter Aiding , 2001, Journal of Navigation.

[4]  O. Yildirim.  Results from a comprehensive GPS Network: natural gas pipeline GPS Network , 2012 .

[5]  Jing Li,et al.  The use of immersive real-time 3D computer graphics for visualisation of dilution of precision in virtual environments , 2010, Int. J. Geogr. Inf. Sci..

[6]  Barry A. Stein.  Satellite Selection Criteria During Altimeter Aiding of GPS , 1985 .

[7]  Jing Li,et al.  Prediction and visualization of GPS multipath signals in urban areas using LiDAR Digital Surface Models and building footprints , 2008, Int. J. Geogr. Inf. Sci..

[8]  Marco Piras,et al.  A Low-Cost Mobile Mapping System (LCMMS) for field data acquisition: a potential use to validate aerial/satellite building damage assessment , 2011 .

[9]  Andreas Schmitt,et al.  SAR polarimetric change detection for flooded vegetation , 2013, Int. J. Digit. Earth.

[10]  Jing Li,et al.  GPS Height Aiding Computation Using Multi-resolution Digital Terrain Models , 2009, 2009 WRI International Conference on Communications and Mobile Computing.

[11]  Fabio Dovis,et al.  Multipath Estimation in Urban Environments from Joint GNSS Receivers and LiDAR Sensors , 2012, Sensors.

[12]  Richard B. Langley.  The Mathematics of GPS , 2005 .

[13]  Martin D. Crossley.  A guide to coordinate systems in Great Britain , 1999 .

[14]  G. Wanielik,et al.  3D environment modeling for GPS multipath detection in urban areas , 2012, International Multi-Conference on Systems, Sygnals & Devices.

[15]  Peter W. Gibbens,et al.  Efficient Terrain-Aided Visual Horizon Based Attitude Estimation and Localization , 2015, J. Intell. Robotic Syst..

[16]  Christian Mandel,et al.  Particle filter-based position estimation in road networks using digital elevation models , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.