An Extreme Learning Machine Correction Network for High Precision Satellite Attitude Determination

The fusion framework of star sensor and gyro based on adaptive Kalman filter is widely used in satellite pose estimation. However, the discretization and linearization inevitably introduce system errors, which degrades of the filtering accuracy. To address this problem, we propose a high-precision satellite attitude determination algorithm based on extreme learning machine network correction. We design a dedicated network for error compensation and trained the parameters effectively. In attitude calculation procedure, the forward fusion filtering of star sensor and gyro data is performed firstly by using the adaptive Kalman filter. Then the filtering estimation results are compensated by the extreme learning machine network proposed in this paper. After that, backward smoothing is performed to solve the high-precision attitude. Simulation results show that armed with the compensation procedure of the proposed extreme learning machine network, the accuracy of estimated pose is significantly improved.