Advances in Gamma Level Measurement by Optimal Autoregressive Kalman Filter

Level measurement in industrial processes plays a major role in process control and monitoring. In hazardous, corrosive and turbulent environments, conventional methods do not have proper efficiency and accuracy and cause measurement errors and disruption in the production process. Due to the level of measurement conditions that the devices must withstand to achieve their goal of accuracy, linearity and reliability, some advanced methods such as gamma-ray measurement are suitable. Kalman filters are a suitable process for non-stationary data and noise reduction. The adaptive filter is presented with an autoregressive prediction model, and the Akaika criterion is studied in this paper for the estimation and smoothing of gamma-level measurement data. Due to the receiver’s uncertainty and distortion of gamma radiation, an improved adaptive Kalman filter with autoregressive and Akaika criterion is proposed to smooth and improve the measurement. Also, the endorsement of the advanced technique has been authenticated by implementation and testing in the industry.