Model-less prediction filter for adaptive adjustment process noise

In this study, a filtering scheme suitable for high-precision sensors was proposed to extract high-precision sensor information. According to the principle of Kalman gain based on data fusion, a model-less prediction filter with minimum gain measurement noise compensation and process noise posteriori constraint adjustment was developed. In comparison to various Kalman filter methods, the proposed algorithm demonstrated better accuracy in the steady state. The high precision performance and effectiveness of the model-less prediction filter were verified under a digitally controlled linear power supply.

Automated summary

In this study, a filtering scheme was proposed to extract high-precision sensor information. The scheme utilized a model-less prediction filter based on the principle of Kalman gain, which compensated for gain measurement noise and adjusted process noise. Compared to various Kalman filter methods, the proposed algorithm demonstrated better accuracy in the steady state. The high precision performance and effectiveness of the model-less prediction filter were verified under a digitally controlled linear power supply.