Jul 2026· Digital Signal and Computer Communications· Vol 14294, pp. 142940F - 142940F-6· 0 citations· 7 references
Engineering
TL;DR
A lightweight fusion model of LSTM and attention mechanism is proposed that adopts a single-layer LSTM to extract temporal features from attitude data and combines with the attention mechanism to enhance the ability to focus on key information.
Abstract
Aiming at the deficiencies of traditional satellite attitude prediction methods in prediction accuracy, feature extraction capability and on-board applicability, this paper proposes a lightweight fusion model of LSTM and attention mechanism. The model adopts a single-layer LSTM to extract temporal features from attitude data and combines with the attention mechanism to enhance the ability to focus on key information. Multiple data preprocessing strategies are introduced to improve the quality of input data, and a relatively complete comprehensive evaluation system is constructed. Experimental results show that the model can realize the joint prediction of three-axis attitude angles and three types of disturbance torques including gravitational gradient torque, solar radiation pressure torque and magnetic torque. The prediction results are in good agreement with the measured data, which can provide effective support for satellite attitude control systems.
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