Sharif Journal of Mechanical Engineering

Sharif Journal of Mechanical Engineering

Attitude Determination of Geosynchronous Satellites with Sun and Earth Sensors Using Artificial Intelligence Algorithms

Document Type : Article

Authors
Farid Hajiweisah 1
Hossein Nejat Pishkenari 1
Taleb Abdollahi 2
1 Faculty of Mechanical Engineering, Sharif University of Technology, Tehran, Iran
2 Iranian Space Research Center, Tehran, Iran
10.24200/j40.2026.68496.1761
Abstract
Attitude determination is a critical component in the design and operation of satellites. This study presents a novel framework for estimating the attitude of a geosynchronous satellite by combining Sun and Earth sensors with artificial intelligence algorithms. Using raw sensor data led to poor or unsatisfactory results, with the best prediction error exceeding 10 degrees; therefore, the raw data were augmented through a preprocessing step utilizing the TRIAD algebraic method. Several machine learning and deep learning algorithms, including XGBoost, CatBoost, and multilayer perceptron (MLP) networks, were then employed to estimate the satellite's rotation quaternions. The findings demonstrated that the simultaneous and parallel use of four identical two-layer MLP models, each predicting a single quaternion component from the preprocessed data, coupled with output normalization, yielded a mean absolute error of 0.57 degrees. This approach outperformed the TRIAD algorithm by approximately 10%, the best classical method evaluated, and its computation time was shown to be compatible with real-time onboard execution. This hybrid approach offers a cost-effective and reliable solution for implementing attitude determination systems in satellites.
Keywords
Attitude determination
Satellite
Artificial intelligence
Sun sensor
Earth sensor
Subjects
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