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Satellite attitude identification and prediction based on Neural Network compensation - EurekAlert
The attitude determination of low earth orbit (LEO) satellite is essential for the normal operation such as communication, maneuver and telemetry etc. Under normal circumstances, the satellite is equipped with infrared earth sensors and star sensors, which can achieve precise attitude determination in real time. However, at the end of the satellite service life, or serious malfunctions occur in satellite electronic system, the attitude determination system is unable to function properly. During this process, accurate satellite attitude prediction without the assistance of sensors is very critical, which can help to determine the condition of satellite debris, estimate the landing area, and reduce the damage caused by debris in advance. In a research paper recently published in Space: Science & Technology, Shengping Gong, from Beihang University, proposed a feedback attitude prediction algorithm to achieve the current and persistent attitude prediction with high accuracy, and designed a high-order torque identification framework based on EKF to reduce observation noise and effectively extract uncertain environmental torque.
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