Analog sun sensor measurement correction using deep neural network

Sun sensors are commonly used attitude sensors because of their low cost, mass, volume, and power con-sumption. Analog Sun sensors (ANSS), which are smaller but usually less accurate than digital ones, are espe-cially preferred for small satellite missions. One of the main reasons for the lesser accuracy of analog Sun sensors is being prone to external errors, most prominently the Earth's albedo. This study proposes an analog Sun sensor calibration method using the Deep Neural Network (DNN). The main contribution of the proposed algorithm is that it does not require any model for measurement correction. The method is tested with simulations and real data from an Earth-imaging spacecraft. Results show that the error in the Sun direction measurements, which can be as high as 10 degrees, can be decreased to a level of 0.5 degrees by using the DNN for calibration. Moreover, testing in different scenarios verifies that the DNN can correct the measurements for periods as long as 7 days without requiring excessive training periods, even when the spacecraft is not in the same flight configuration for which the DNN was trained.

Automated summary

Sun sensors are commonly used for attitude sensing due to their low cost and small size. This study proposes a calibration method using the Deep Neural Network (DNN) to improve the accuracy of analog Sun sensors. The DNN-based method does not rely on a model for measurement correction and can reduce the error in Sun direction measurements from 10 degrees to 0.5 degrees. The DNN can also correct measurements for different scenarios without extensive training periods.