Optimizing the Use of RTKLIB for Smartphone-Based GNSS Measurements

Abstract

The Google Smartphone Decimeter Challenge (GSDC) was a competition held in 2021, where data from a variety of instruments useful for determining a phone’s position (signals from GPS satellites, accelerometer readings, gyroscope readings, etc.) using Android smartphones were provided to be processed/assessed in regard to the most accurate determination of the longitude and latitude of user positions. One of the tools that can be utilized to process the GNSS measurements is RTKLIB. RTKLIB is an open-source GNSS processing software tool that can be used with the GNSS measurements, including code, carrier, and doppler measurements, to provide real-time kinematic (RTK), precise point positioning (PPP), and post-processed kinematic (PPK) solutions. In the GSDC, we focused on the PPK capabilities of RTKLIB, as the challenge only required post-processing of past data. Although PPK positioning is expected to provide sub-meter level accuracies, the lower quality of the Android measurements compared to geodetic receivers makes this performance difficult to achieve consistently. Another latent issue is that the original RTKLIB created by Tomoji Takasu is aimed at commercial GNSS receivers rather than smartphones. Therefore, the performance of the original RTKLIB for the GSDC is limited. Consequently, adjustments to both the code-base and the default settings are suggested. When implemented, these changes allowed RTKLIB processing to score 5th place, based on the performance submissions of the prior GSDC competition. Detailed information on what was changed, and the steps to replicate the final results, are presented in the paper. Moreover, the updated code-base, with all the implemented changes, is provided in the public repository. This paper outlines a procedure to optimize the use of RTKLIB for Android smartphone measurements, highlighting the changes needed given the low-quality measurements from the mobile phone platform (relative to the survey grade GNSS receiver), which can be used as a basis point for further optimization for future GSDC competitions.

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

“…The RTKLIB package [ 46 ], developed by Tomiji Takasu, is one of the most comprehensive open source software packages for GNSS data processing. Based on this code, but with the aim of improving solutions for lower cost receivers and lower quality data, a fork of RTKLIB code named Demo5 has been developed by Tim Everett and is maintained on GitHub [ 47 ]. The repository contains code versions as well as changelogs describing the particular changes and enhancements to the code.…”

“…The configuration file for the F9P solutions in this set was based on a configuration file included with this code that is optimized for the F9P receiver. The configuration file for the Pixel 5 solutions in this set were based on earlier work done with cellphone datasets for the 2021 and 2022 Google Smartphone Decimeter Challenges [ 19 , 48 ]. In both cases, the configuration files were further optimized based on specific environmental conditions of this data and with some iteration of solutions on a subset of the data.…”

“…Simple, omnidirectional antennas used in smartphones are often considered the main reason for such an inferiority. The other group of studies is focused on practical applications, taking into account many differing positioning approaches, including single point positioning (SPP) [ 11 , 12 ], precise point positioning (PPP) [ 13 , 14 ], differential GNSS (DGNSS) [ 15 , 16 ], real-time kinematics (RTK) [ 17 , 18 ], post-processed kinematics (PPK) [ 19 ] and static (carrier-phase based) positioning [ 20 ]. To supplement the low-quality GNSS data from smartphones, authors have recently focused on combinations with other sensors usable for positioning, e.g., inertial measurement units (IMU) [ 21 , 22 , 23 ].…”

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

“…Similar to [ 35 ], the observation values of valid GNSS signals are selected based on the pseudorange tracking status contained in the smartphone logs. In addition, the pseudorange residuals are calculated using the initial 3D position and clock bias described above.…”

“…From the carrier-phase tracking status, the carrier phases continuously tracked with no cycle slip are reserved [ 35 ]. However, even in the carrier phases that have passed these processes, some carrier phases that contain cycle slips or errors still remain.…”