Research on High-Precision Filtering Algorithm of Radiation Dosimeter

The measurement result of embedded multi-channel radiation dosimeter is easy to be interfered by the system itself and the environment noise, which affects the measurement accuracy. In this paper, a fuzzy Kalman small-range adaptive filtering method was proposed to improve the measurement accuracy and save the memory space. Firstly, the filtering residual of Kalman filter was extracted as the filtering relative error, and a reasonable threshold was set for the filtering relative error by analyzing the environmental noise characteristics of the radiation dosimeter. On this basis, a fuzzy controller was designed, taking the difference between the relative error of real-time filter and the error threshold as the input of the fuzzy controller, and the process noise covariance Q of Kalman filter as the output. The basic discourse domain of fuzzy logic input and output was set up, the membership function was established, and fuzzy control rules were formulated according to the measurement experience. The process noise covariance Q was adaptive to the relative error of the filter in the dynamic radiation stage and the constant radiation stage, and the measurement accuracy of the dosimeter was improved. When the dosimeter is placed in the environment with the dose rate of 0 μSv/h～15Sv/h, the maximum dose rate error of the dosimeter using the method described in this paper is 4. 3% and 8. 3% respectively in the dynamic and constant radiation, the cumulative dose error of 180s is about 10%. Compared with the moving average filtering method and the Kalman filtering method based on residual judgment, the fuzzy Kalman small-range adaptive filter proposed in this paper has more advantages in embedded multi-channel dosimeter.