Research on non-stationary noise suppression method based on improved adaptive Kalman filter

In the process of modern industrial production, the sensor signal is often disturbed by non-stationary noise, which affects the monitoring accuracy, decision accuracy and system operation efficiency. In this paper, a non-stationary noise suppression method based on Adaptive Kalman Filtering (AKF) is proposed. By introducing new techniques such as dynamic noise estimation module, multi-stage filtering, state detection mechanism, amplitude constraint and error band, this method effectively improves the filtering performance and enhances the robustness of the algorithm. At the same time, a visual analysis system with 3σ error confidence band is developed. The experimental results show that, compared with the standard Kalman filter, the signal-to-noise ratio (SNR) of the proposed method is increased by 79.3%, the mean square error is reduced by 55.6%, and the signal delay is reduced by 54.4% in the complex multi-frequency signal denoising task. This study provides an efficient and accurate solution for industrial signal processing.