All observations inevitably contain outliers.Outlier detection is a particularly tough problem under the complex conditions of the combination of multi-frequency and multi-constellation and urban canyon environments.Here,outlier detection is studied to ensure accurate and reliable positioning performance of BDS/GNSS data.The outlier detection schemes with different thresholds were proposed based on mean shift and variance inflation.Then dedicated experiments under complex conditions were carried out to evaluate the performance of the proposed outlier detection schemes.The experimental results show that the RMSE of the mean shift based small threshold(U_1/2α2(0,1)) scheme is improved by 0.059 m,0.017 m,and 0.062 m in the E,N,and U directions,respectively.The RMSE of the combined large iterative threshold(ω) and small threshold (k₀、k₁) scheme based on variance inflation is improved by 0.098 m,0.055 m,and 0.209 m in the E,N,and U directions,respectively.The results show that the above two schemes can detect and identify outliers under complex conditions.