Enhancing Smartphone Relative Positioning With Partial Wide-Lane Ambiguity Resolution: Path to Real-Time, Decimeter-Level Positioning in User Environments

The ubiquity of smartphones catalyzes myriad smartphone-based Internet of Things (IoT) applications, amongst which smartphone positioning which utilizes global navigation satellite system (GNSS) observations to provide spatial information plays a crucial role. However, noisy smartphone GNSS measurements prevent decimeter-level positioning performance in user environments. Recovering the integer property of GNSS carrier phase measurement ambiguities shows great potential in achieving high-accuracy positioning solutions. However, inaccurate ambiguity estimates and short signal wavelengths are the main barriers to successful ambiguity resolution (AR). Therefore, a partial AR with wide-lane (WL) ambiguities and an automatic ambiguity hold strategy is proposed. Simulated results show that, for single-epoch WL AR, even with one WL ambiguity correctly fixed, the positioning solution can be improved by 3 cm. With actual static and kinematic datasets, the proposed algorithm is evaluated and validated. Static results show an improvement of 83% in horizontal position when the WL AR approach is applied, and positioning accuracies can reach 6.8, 2.9, and 11.5 cm in the E, N, and U direction components, respectively. For kinematic data collected in highly variable realistic driving environments, the time series of positioning errors of WL AR solutions exhibit less variation than float solutions. And with fixed WL ambiguities, solutions can be improved to varying degrees, ranging from several centimeters to up to 8 dm depending on the environment. The largest improvement of 8 dm is observed for 95th percentile horizontal positioning errors under a suburban environment.