LAUSR

Radiometer calibration using a compensation method based on temperature drift under a variety of environmental conditions can be used for airborne applications because of limitations on weight and power. Accordingly, the observation of the variation of the system states via analysis of the obtained response with temperature data is necessary to compensate the fluctuation of physical temperature for accurate calibration of the radiometer. However, the accuracy of the previous method (which uses one or more temperature probe) can rarely obtain an accurate estimated result; its accuracy is insufficient for high-precision measurement applications. Additionally, previous methods may cause unpredictable estimation errors when using regression methods with fewer measurable sensors. Therefore, more advanced compensation methods, which can compensate for the drift in radiometer output using temperature sensors (based on the analysis of the distinctive features of the temperature), are required to estimate the brightness temperature. In this paper, an optimized thermal compensation method using a selection of the optimal point for gain and offset probing to control the coefficients is analyzed using both the correlation-matrix-based hierarchical clustering and a comparison with the response stability for the training period. Then, the clustered temperature sensors of the radiometer system are used in the predictor variable to achieve optimum compensation of radiometer response variation. Next, the regression model of the multiple linear clustering method is compared the estimation accuracy for the selected training period and temperature sensors. Improved results relative to the reference method are achieved using the proposed model in experiments.