This presentation contains two subtopics: GNSS orbit prediction and Parametric Fingerprint Positioning Methods Using Crowdsourced Data.
Orbit prediction algorithms can be used in a portable positioning device to reduce the Time to First Fix and to augment the received broadcast ephemeris. In this presentation, we present the two-week prediction accuracy improvement that can be obtained when adding some smaller forces to our previously developed algorithm. These forces arise from solid earth tides, relativity effect, and the gravitational pull of Venus and Jupiter. Also, a box-wing model of solar radiation pressure is considered. The new model with aforementioned extra forces is tested for GPS, GLONASS and Beidou satellites using initial conditions computed from precise ephemerides. It is found that the enhancements give small but not negligible improvement, with more accurate Sun and Moon coordinates having the most effect and relativity correction for Earth’s gravity the least. However, the improvements come at the cost of noticeable increase in computational load.
The term fingerprint-based (FP) positioning includes a wide variety of methods for determining a receiver’s position using a database of radio signal strength measurements that were collected earlier at known locations. Nonparametric methods such as the weighted k-nearest neighbour (WKNN) method are infeasible for large-scale mobile device services because of the large data storage and transmission requirements. In this presentation, we present an overview of parametric FP methods that use model-based representations of the survey data. We look at different groups of parametric methods: methods that use coverage areas and methods that use path loss models. We analyse the positioning performance of those methods using real-world WLAN indoor data and compare the results with those of the WKNN method. We addressed the problem where we do not know the locations of fingerprints and how we could still utilize these unlocated fingerprints.