## 5 Conclusions  
GNSS/INS integrated navigation possesses excellent characteristics so that it plays a significant role in vehicle positioning requirements. However, the performance of GNSS/INS integration suffers from excessive unexpected GNSS outliers such as multipath/NLOS signal in dense urban areas. This paper put forward an urban vehicle GNSS multipath/NLOS observation detection algorithm based on K-means clustering, which can effectively promote the accuracy of GNSS/INS tightly coupled positioning results. The method is essentially an offline learning system that can be used for post-processing solution of GNSS/INS observation data. Simultaneously, we employ K-means to detect observation outliers and obtain LOS/NLOS classification rules, which can be further broadened to GNSS/INS integrated navigation vehicle position in real-time. The proposed method obtains the signal type label by sufficiently excavating the information of the GNSS observation data itself, without the assistance of external software and hardware. Based on the good scalability of the K-means clustering algorithm, the above model is used to identify the multipath/NLOS of online observation data for real-time positioning. As a result, it can effectively enhance the performance of GNSS/INS tightly coupled system with higher availability and environmental adaptability. In future work, we will continue to research the influence of the GNSS signal distribution pattern in different scenarios and test data on the positioning performance of GNSS/INS tightly coupled system, and study a more robust outlier boundary determination rule. Additionally, when the number of visible satellites is relatively small, simply excluding the multipath/NLOS signal will deteriorate the satellite geometric distribution, which reduces the positioning accuracy or even fails to execute the positioning solution. Therefore, in future research, we will further consider reasonable multipath/NLOS processing strategies, such as optimizing the stochastic model of the observation equation [45]. [PERSON] conceived the idea and designed the experiments with [PERSON] and [PERSON], [PERSON] and [PERSON] wrote the main manuscript. [PERSON] reviewed the paper. [PERSON] collected GNSS/INS dynamic observation by vehicle. All components of this research were carried out under the supervision of [PERSON]. All authors have read and agreed to the published version of the manuscript. **Funding:** This work is partially supported by the National Key Research and Development Program of China (Grant No. 2021 YFB3900804) and the Research Fund of Ministry of Education of China and China Mobile (Grant No. MCM20200J01). **Data Availability Statement:** The data presented in this study are available on request from the corresponding author. **Acknowledgments:** We would like to thank everyone who contributed to this research, including data collection, manuscript review and project support. **Conflicts of Interest:** The authors declare no conflict of interest. * [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON] GNSS outlier mitigation via graduated non-convexity factor graph optimization. _IEEE Trans. Veh. Technol._**2021**, _71_, 297-310. [CrossRef]
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