Multi-Constellation GNSS Performance Evaluation for Urban Canyons Using Large Virtual Reality City Models

Abstract

Positioning using the Global Positioning System (GPS) is unreliable in dense urban areas with tall buildings and/or narrow streets, known as 'urban canyons'. This is because the buildings block, reflect or diffract the signals from many of the satellites. This paper investigates the use of 3-Dimensional (3-D) building models to predict satellite visibility. To predict Global Navigation Satellite System (GNSS) performance using 3-D building models, a simulation has been developed. A few optimized methods to improve the efficiency of the simulation for real-time purposes were implemented. Diffraction effects of satellite signals were considered to improve accuracy. The simulation is validated using real-world GPS and GLObal NAvigation Satellite System (GLONASS) observations. The performance of current and future GNSS in urban canyons is then assessed by simulation using an architectural city model of London with decimetre-level accuracy. GNSS availability, integrity and precision is evaluated over pedestrian and vehicle routes within city canyons using different combinations of GNSS constellations. The results show that using GPS and GLONASS together cannot guarantee 24-hour reliable positioning in urban canyons. However, with the addition of Galileo and Compass, currently under construction, reliable GNSS performance can be obtained at most, but not all, of the locations in the test scenarios. The modelling also demonstrates that GNSS availability is poorer for pedestrians than for vehicles and verifies that cross-street positioning errors are typically larger than along-street due to the geometrical constraints imposed by the buildings. For many applications, this modelling technique could also be used to predict the best route through a city at a given time, or the best time to perform GNSS positioning at a given location.

RETRACTED ARTICLE: Likelihood-based GNSS positioning using LOS/NLOS predictions from 3D mapping and pseudoranges

“…This is done efficiently by comparing the satellite elevation with that of a precomputed building boundary at the appropriate azimuth (Wang et al 2012).…”

“…These describe the minimum elevation above which satellite signals can be received at a series of azimuths. A signal can then be classified as LOS or NLOS simply by comparing the satellite elevation with that of the building boundary (Wang et al 2012). The 3D building models can also be used to predict the additional path traveled by NLOS signals, enabling affected pseudoranges to be corrected.…”

Applications of 3D City Models: State of the Art Review

“…For instance, they are used in estimating the visibility of a landmark [182,183], assessing façade visibility for city marketing [184,185], in determining the optimal location for surveillance cameras [186][187][188], sensor coverage assessment [189], improving road safety [190], assessing sniper hazards [191], and in real estate mass valuation in the urban areas, based on the assumption that the view from an apartment is one of the factors driving its price [192][193][194]. Further applications involve predicting the visibility of GNSS satellites in the built environment and mitigating the multipath effect [195][196][197][198][199][200][201][202][203][204]. Such methods are valuable for enhancing map matching for navigation in urban canyons [205].…”

Smartphone Shadow Matching for Better Cross-street GNSS Positioning in Urban Environments

“…Figure 5 shows an example. The elevation of the building boundary is computed at a range of azimuths as described in Wang et al (2013a) and Wang et al (2012). Building boundaries are computed over a grid of candidate user locations.…”