# Optimizing the Use of RTKLIB for Smartphone-Based GNSS Measurements
## 4 Summary and Conclusions  
The latest version of the RTKLIB code, with the aforementioned changes and settings, was able to obtain 5 th place ranking, with a score of 2.15. This is a significant improvement compared to the Google baseline score of 5.42, which implies that for the individuals using RTKLIB as the GNSS analysis tool, the provided tools, strategies, and updated code base provide an improved baseline from which to work. The code for this analysis used doppler-carrier comparison, geometry-free linear combination, and outlier detection methods for cycle slip detection, but did not incorporate any other cycle slip detection or mitigation methodologies, measurement error analysis, multipath mitigation, Kalman filter tuning, nor any of the numerous post-processing techniques proposed in various academic publications. Therefore, further performance improvements are expected with their additions. Furthermore, the conversion from the GnssLogger to RINEX can be further investigated, along with the optimization of the receiver parameter settings. The improvement in the GNSS-derived positions compared to Google's baseline solution is a crucial component of GSDC. This is because the competition has essentially two components: deriving accurate GNSS positions, and post-processing the GNSS solutions. The updated RTKLIB addresses the first component by proposing methods to improve the accuracy of the GNSS solutions and provide the resulting positions. This opens the door to more possibilities in the second component. For those who use Google's baseline solution for post-processing, the RTKLIB GNSS positions will have the potential to provide results with higher accuracy. One of the strategies for the post-processing of the GNSS positions would be map matching with respect to the truth locations of the train data, as the drive trajectories on the road would be similar between collections. All of the updates for the special GSDC version of RTKLIB are currently in the mainstream demo5 code in the latest b34f release. Moreover, a subset of the demo5 RTKLIB PPK solution code translated to python is available as a base for users who are interested in performing more involved experiments that can be difficult to develop in the original C code environment. Detailed steps required to replicate the results provided in this paper are provided in the blog [22]. Conceptualization, T.E. ; methodology, T.E. ; software, T.E. ; validation, T.T. and D.-K.L. ; formal analysis, D.-K.L. ; investigation, T.T. and D.-K.L. ; writing-original draft preparation, T.T. and D.-K.L. ; writing-review and editing, T.E., T.T., D.-K.L., D.M.A. ; visualization, T.E., D.-K.L. ; supervision, D.M.A. ; project administration, D.M.A. ; funding acquisition, T.E. All authors have read and agreed to the published version of the manuscript. **Funding:** General financial support for the RTKLIB demo5 code was provided through Open Collective. The donor for the project at the time of this publication was Google Open Source. [[https://opencollective.com/rtklib-demo5](https://opencollective.com/rtklib-demo5)]([https://opencollective.com/rtklib-demo5](https://opencollective.com/rtklib-demo5)), accessed on 13 April 2022. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The GSDC data can be found on the official Kaggle website: [[https://www.kaggle.com/c/google-smartphone-decimeter-challenge/data](https://www.kaggle.com/c/google-smartphone-decimeter-challenge/data)]([https://www.kaggle.com/c/google-smartphone-decimeter-challenge/data](https://www.kaggle.com/c/google-smartphone-decimeter-challenge/data)), accessed on 13 April 2022. The observation data for the nearby CORS station can be found at NOAA: [[https://www.ngs.noaa.gov/UFCORS/](https://www.ngs.noaa.gov/UFCORS/)]([https://www.ngs.noaa.gov/UFCORS/](https://www.ngs.noaa.gov/UFCORS/)), accessed on 13 April 2022.