Algorithms for Mapping the Urban Signal Environment for Navigation of Ground Vehicle Operations

Abstract

GPS positioning in urban environments is challenging as tall buildings often block, reflect, or diffract signals. These signals then reach the receiver via a non-line-of-sight (NLOS) path, which may result in positioning errors such that a vehicle using conventional GPS will need to slow down or reroute its course. Improvements can be made through urban mapping that allows the specularity and visibility to be predicted at any location of interest within the mapped environment. Building footprints are raytraced to create a database of visibility and specular skyplots, which display the expected sky visibility and likelihood of specular reflections at any given location. Direct position estimation (DPE) is an alternative positioning approach that directly computes a multi-dimensional spatial correlogram that can be analyzed to determine the most likely user location. This approach requires a priori information about satellites, user location and clock bias, which are all readily established in urban environments. A driving experiment was performed with an equipped vehicle in downtown Denver CO. By examining several regions of interest with varying degrees of conventional GPS positioning success, the use of DPE as well as incorporating visibility and specularity predictions has been shown to improve positioning estimates.