Optimizing the Use of RTKLIB for Smartphone-Based GNSS Measurements

Static Positioning under Tree Canopy Using Low-Cost GNSS Receivers and Adapted RTKLIB Software

The decrease in costs and dimensions of GNSS receivers has enabled their adoption for a very wide range of users. Formerly mediocre positioning performance is benefiting from recent technology advances, namely the adoption of multi-constellation, multi-frequency receivers. In our study, we evaluate signal characteristics and horizontal accuracies achievable with two low-cost receivers-a Google Pixel 5 smartphone and a u-Blox ZED F9P standalone receiver. The considered conditions include open area with nearly optimal signal reception, but also locations with differing amounts of tree canopy. GNSS data were acquired using ten 20 min observations under leaf-on and leaf-off conditions. Post-processing in static mode was conducted using the Demo5 fork of the RTKLIB open source software, which is adapted for usage with lower quality measurement data. The F9P receiver provided consistent results with sub-decimeter median horizontal errors even under tree canopy. The errors for the Pixel 5 smartphone were under 0.5 m under open-sky conditions and around 1.5 m under vegetation canopy. The adaptation of the post-processing software to lower quality data was proven crucial, especially for the smartphone. In terms of signal quality (carrier-to-noise density, multipath), the standalone receiver provided significantly better data than the smartphone.

Precise Position Estimation Using Smartphone Raw GNSS Data Based on Two-Step Optimization

This paper presents a high-precision positioning method using raw global navigation satellite system (GNSS) observations from smartphones in the Google smartphone decimeter challenge (GSDC). Compared to commercial GNSS receivers, smartphone GNSS observations are noisy owing to antenna limitations, making it difficult to apply conventional high-precision positioning methods. In addition, it is important to exclude outliers in GSDC because GSDC includes data in environments where GNSS is shielded, such as tunnels and elevated structures. Therefore, this study proposes a smartphone positioning method based on a two-step optimization method, using factor graph optimization (FGO). Here, the velocity and position optimization process are separated and the velocity is first estimated from Doppler observations. Then, the outliers of the velocity estimated by FGO are excluded, while the missing velocity is interpolated. In the next position-optimization step, the velocity estimated in the previous step is adopted as a loose state-to-state constraint and the position is estimated using the time-differenced carrier phase (TDCP), which is more accurate than Doppler, but less available. The final horizontal positioning accuracy was 1.229 m, which was the first place, thus demonstrating the effectiveness of the proposed method.