Urban Positioning on a Smartphone: Real-time Shadow Matching Using GNSS and 3D City Models

The poor performance of global navigation satellite system (GNSS) user equipment in urban canyons is a well-known problem, particularly in the cross-street direction. However, the accuracy in the cross-street direction is of great importance in Intelligent Transportation Systems (ITS) and land navigation systems for lane identification, in location based advertisement (LBA) for targeting suitable consumers and many other location-based services (LBS). To tackle this problem, a new approach, shadow matching, has been proposed, assisted by knowledge derived from 3D models of buildings. In this work, a new smartphone-based positioning system using 3D city models is designed. The system is then implemented in an application (app, or software) on the Android operating system. With a number of optimizations developed, for the first time, a demonstration is performed on a smartphone using real-time GPS and GLONASS data stream. The computational efficiency of the system is thus verified, showing its potential for larger scale deployment. Experiments were conducted at four different locations, providing a statistical performance analysis of the new system. Analysis was conducted to evaluate the performance of the system. The experimental results show that the proposed system outperforms the conventional GNSS positioning solution provided by GNSS chips on smartphones, reducing the cross-street positioning error by 69.2 % on average. It should be noted that the system does not require any additional hardware or real-time rendering of 3D scenes. It is therefore power-efficient and cost-effective. The system is also expandable to work with Beidou (Compass) and Galileo in the future, with potentially improved performance.