With the rapid development of autonomous driving, the demand for high-precision real-time vehicle navigation and positioning technology is becoming increasingly urgent. In the commonly used GNSS/INS integrated navigation, adaptive Kalman filtering is a standard state prediction method. However, in complex dynamic environments, it has limitations in dealing with multipath noise from GNSS and real-time variations in process noise. To address this issue, this paper proposes an adaptive anti-noise Kalman filtering algorithm to suppress measurement noise from GNSS and dynamic process noise. The algorithm first preprocesses the original GNSS measurement data using variational mode decomposition and wavelet denoising to improve the input accuracy for data fusion. Secondly, during the data fusion process, a dynamic noise scaling factor that changes in real time with the vehicle environment is introduced. Through these two denoising steps, the overall interference of noise uncertainty on navigation accuracy is effectively suppressed. The effectiveness of the proposed method is verified through simulations and real vehicle experiments. Compared with the traditional adaptive Kalman filtering algorithm, the proposed algorithm reduces the position estimation error and speed estimation error by 37.7% and 42.8%, respectively, significantly enhancing the high-precision estimation capability of vehicle speed and position.