In view of the problems of outlier and noise in tunnel structure safety monitoring data, which seriously affect the subsequent analysis. A reliable Kalman filter algorithm for data denoising of adaptive tracking system is proposed. Firstly, the least square method is used to compensate the outliers with sliding Window. Secondly, it inherits Kalman’s idea of “step by step derivation” and dynamically estimates the noise value, which effectively solves the problem that the traditional Kalman Filter cannot accurately model when facing outliers and nonlinear systems. Finally, the data of a construction subway in Beijing are used for numerical verification, and the results show that the proposed algorithm has a great improvement in accuracy compared with the classical algorithms.