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Under the influence of urban building roads, especially interference from multipath effects, global navigation satellite system (GNSS) receiver-related output signal distortion can affect the robustness of the positioning system and the final positioning accuracy. To deal with the above problems, this paper proposes a two-layer consistency-checks (CC) positioning model based on eXtreme Gradient Boosting (XGBoost) integrated learner. First, the model excludes the abnormal values from the correlated output of the first layer by the classical statistical distribution test method. Then, the remaining available measurements are used as the second-layer input, and the measurements are used as learning data using an integrated machine learning method, XGBoost, to efficiently detect and identify non-line-of-sight (NLOS), LOS, and other reflective multipath signals. In order to better mitigate errors in the dynamic relative positioning process, the second-layer checking process uses dynamic pseudorange differencing technique (DPDT) and weighted least squares method (WLS) to smooth the output outcome of the receiver. In the experimental part, we compare and analyze the proposed method with the existing methods from different perspectives in this paper, respectively. The results show that the performance of the model is significantly improved after applying the CC method, in which the average classification accuracy of the multipath signals in the target feature set can reach 91.6%. According to the final positioning results, the proposed method shows a significant accuracy improvement compared to the existing research methods.