During the integrated navigation process, the traditional Sage-Husa adaptive filter often suffers from decreased filtering accuracy and even divergence due to external disturbances. To address these challenges, this paper proposes an improved Kalman filter-based integrated navigation algorithm using multiple fading factors. The algorithm dynamically adjusts the predicted state covariance matrix through multiple fading factors to suppress filtering anomalies in high-dynamic environments. Meanwhile, control factors are constructed based on the error characteristics of different state components and combined with the abnormal measurement noise detection conditions to correct the impact of abnormal noise on the integrated navigation system. Simulation results show that when applied to the GNSS/SINS integrated navigation system, the algorithm can accurately track the changes in measurement noise of each state component, effectively prevent filter divergence, and significantly enhance the system's stability and filtering accuracy.