Star Image Prediction and Restoration under Dynamic Conditions

Area of Science:

• Spacecraft attitude determination
• Optical sensing technology
• Robotics and control systems

Background:

• Star sensors are crucial for spacecraft attitude control.
• High dynamic conditions cause star image issues like frame loss and smearing, impacting accuracy.
• Existing methods struggle with dynamic environments, leading to potential failures in attitude determination.

Purpose of the Study:

• To enhance star sensor performance under high dynamic conditions.
• To address frame loss and image smearing issues in star sensors.
• To improve the accuracy and reliability of spacecraft attitude determination.

Main Methods:

• A gyroscope-assisted star image prediction model is developed to compensate for frame loss, considering lens distortion.
• The point spread function (PSF) for deblurring smeared star images is calculated using gyroscope angular velocity.
• An improved Richardson-Lucy (RL) algorithm, enhanced by an ensemble back-propagation neural network (EBPNN), is used for star image deblurring.

Main Results:

• The proposed method effectively predicts star spot positions, mitigating frame loss issues.
• The gyroscope-assisted deblurring restores smeared star images with improved clarity.
• The EBPNN accurately predicts the optimal number of iterations for the RL algorithm, ensuring efficient deblurring.

Conclusions:

• The integrated approach significantly improves star sensor accuracy and reliability in dynamic environments.
• The method offers a robust solution for attitude determination challenges in high-maneuver spacecraft.
• Simulation results confirm the effectiveness and real-time applicability of the proposed gyroscope-assisted star image processing technique.