# An Algorithm to Assist the Robust Filter for Tightly Coupled RTK/INS Navigation System
## 6 Conclusions  
This study introduces the TC RTK/INS integration implementation in detail. We give some derivations of key issues, including the formulation of the observation matrix and the process to obtain the fixed solution. We open source our implementation to help save the effort and cost of the researchers who are in the same field as us. These codes can be found at [[https://github.com/Nronaldo](https://github.com/[PERSON])]([https://github.com/[PERSON]](https://github.com/[PERSON])), accessed on 2 April 2022. We test the positioning performance of the TC RTK/INS integration on two open-source urban area datasets. The results show that the introduction of INS improves the positioning continuity and the fixed rate. As outlying measurements frequently appear in urban areas, the TC RTK/INS integration without the outlier resistance cannot obviously improve the positioning accuracy. The IGG-based TC RTK/INS integration uses an inflation factor to suppress the effects of the outliers, which significantly improves the positioning accuracy for both datasets. As excessive robustness occasionally happens, we propose a CNR-based algorithm to decide whether a detected outlier should be maintained for a while or not. Our proposed algorithm improves the positioning accuracy for the Shinjuku dataset, which presents evident excessive robustness. When it comes to the Odaiba dataset without obvious excessive robustness, our proposed algorithm will not degrade the positioning accuracy.  
There is still a lot of room for improvement in our work. Two datasets can only verify the algorithm feasibility on some occasions. Furthermore, our open-source implementation is only a preliminary architecture. It does not represent the state of the art. The architecture  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Techniques** & **RMSE-X (\(m\))** & **RMSE-Y (\(m\))** & **RMSE-Z (\(m\))** & **Total RMSE (\(m\))** & **Fixed Rate** \\ \hline RTK & 3.25 & 5.00 & 5.31 & 7.98 & 20.0\% \\ \hline TC RTK/INS (EKF) & 3.58 & 5.58 & 4.39 & 7.95 & 26.1\% \\ \hline TC RTK/INS (RKF) & 0.90 & 1.08 & 1.19 & 1.84 & 26.8\% \\ \hline TC RTK/INS (MRKF) & 0.88 & 1.08 & 1.14 & 1.80 & 26.9\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: The positioning performance comparison between four techniques (Odaiba).  
needs more modules, including reliable cycle slip repair, gross error preprocessing, and fixed solution verification. We will design the hardware platform to collect more datasets in urban areas in the future. We will seek to obtain professional devices and commercial software to provide the reference trajectory and improve our proposed algorithm. We will also develop our current implementation and add new functional modules to our open-source architecture.  
Conceptualization, Z.N.; formal analysis, Z.N., F.G., Q.S., and G.L.; software, Z.N.; writing, original draft, Z.N.; writing, review and editing, Z.N. and B.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Project of China, Grant Number 2020 YFB0505602-02.  
The authors thank the team from Hong Kong Polytechnique University for providing the Tokyo datasets that can be found in [[https://github.com/weisongwen/UrbanNavDataset](https://github.com/weisongwen/UrbanNavDataset)]([https://github.com/weisongwen/UrbanNavDataset](https://github.com/weisongwen/UrbanNavDataset)), accessed on 2 April 2022. The authors thank the authors of RTKLIB, PSINS, and GINAV for selflessly providing open-source codes.  
The authors declare no conflict of interest.