Analysis and Conditioning of GNSS Measurements from Smartphones for Precise Point Positioning in Realistic Environments
Abstract
With the availability of raw GNSS code and carrier-phase measurements from dual-frequency chips in smartphones, Precise Point Positioning (PPP) can be used to improve the accuracy of the positioning solution offered by them, without any additional reference infrastructure.
In realistic applications in suburban and urban environments, with the smartphone in hand or on the dashboard of a car, there are numerous challenges with smartphone data such as missing measurements, poor multipath suppression and low and irregular carrier-to-noise-density ratio. The measurements are analysed and conditioned or pre-processed by implementing a prediction technique for filling in data gaps and a C/N0-based stochastic model for assigning realistic a priori weights to the measurements in the PPP processing engine. Finally, the post-processed PPP solution accuracy and availability have been compared with the positioning solution obtained from other GNSS positioning techniques. After conditioning, the smartphone PPP positioning solution is seen to have nearly 100% solution availability and 50% more accuracy.
In realistic applications in suburban and urban environments, with the smartphone in hand or on the dashboard of a car, there are numerous challenges with smartphone data such as missing measurements, poor multipath suppression and low and irregular carrier-to-noise-density ratio. The measurements are analysed and conditioned or pre-processed by implementing a prediction technique for filling in data gaps and a C/N0-based stochastic model for assigning realistic a priori weights to the measurements in the PPP processing engine. Finally, the post-processed PPP solution accuracy and availability have been compared with the positioning solution obtained from other GNSS positioning techniques. After conditioning, the smartphone PPP positioning solution is seen to have nearly 100% solution availability and 50% more accuracy.