## 1 Introduction
With the prosperity of artificial intelligence technology, more and more scholars began to employ machine learning or deep-learning methods to identify multipath/NLOS signals. These methods constructed the mapping relationship between multiple feature parameters and GNSS signal categories to reduce the operating cost of traditional methods and enhance the availability of the algorithm, which have achieved outstanding results [25; 26; 27; 28]. The above approaches are classifiers based on supervised learning that need to label the training samples in advance. Meanwhile, the accuracy of the labeling is directly associated with the performance of the classifiers. In practical applications, it is challenging and expensive to obtain an accurately labeled dataset that covers multiple scenarios and represents all state types. In order to refrain from the limitations of signal classification methods caused by the above problems, scholars have begun to use unsupervised learning techniques for signal classification research, which can sufficiently excavate the information of the observation data itself [29; 30; 31; 32]. Compared with the supervised-learning multipath/NLOS signal detection algorithm, the unsupervised technology has better advantages in availability and environmental applicability. Furthermore, the positioning accuracy after excluding contaminated GNSS satellites is significantly better than the traditional threshold method and the classic RAIM algorithm. Given that the multipath/NLOS signal restricts the dynamic positioning accuracy and reliability of the GNSS/INS integrated system, apart from GNSS alone positioning, multi-path/NLOS detection and mitigation for GNSS/INS integrated systems were developed in past years [33; 34; 35]. However, these technologies are dependent on geographic information data or external sensor equipment, such as 3D building models, cameras, and LiDAR, which have a certain level of deficiencies in terms of availability, cost, and security. In addition, the equivalent weight model is employed to construct robust algorithms that can weaken the influence of the gross error of the observation on the positioning accuracy [36; 37]. This method utilizes the robust factor to adjust the filter gain moment or the observation noise for GNSS/INS integrated positioning, which plays a role in suppressing multipath and NLOS errors to a certain extent. However, the robust estimation algorithm has difficulty handling multiple outliers on the same epoch and relies heavily on the correctness of the robust model. Therefore, when the original observations are seriously polluted by the multipath/NLOS signals in harsh environments, the reliability of the algorithm cannot be guaranteed. This paper aims to further promote the accuracy of GNSS/INS tightly coupled positioning results by using unsupervised techniques to detect multipath/NLOS signals. A clustering algorithm is utilized to label GNSS data in offline system as normal and abnormal observations, the latter mainly caused by multipath/NLOS signals. The clustering criteria obtained by offline dataset training are applied to detect multipath/NLOS signals for online data, which can enhance the performance of GNSS/INS real-time positioning. Additionally, it can provide an innovative perspective for the research on GNSS signal quality control methods of vehicle positioning systems in highly complex urban areas. The rest of this paper is organized as follows: In Section 2, the relevant mathematical methodology is presented for K-means Clustering and GNSS/INS tightly coupled positioning algorithm. Next, Section 3 implements the data collection and experiment analysis, which validate the accuracy and reliability improvements of the proposed method. Finally, the conclusion and outlook will be given in Section 4.