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    Area of Science:

    • * Optical Engineering
    • * Artificial Intelligence
    • * Aerospace Instrumentation

    Background:

    • * Accurate ground calibration is critical for star sensor performance, particularly for large field-of-view (FOV) systems.
    • * Traditional calibration methods struggle with complex distortions and error coupling, demanding extensive calibration data for improved accuracy.
    • * Existing methods face limitations in achieving high precision without substantial data requirements.

    Purpose of the Study:

    • * To develop a novel, high-precision laboratory calibration method for large FOV star sensors.
    • * To reduce the dependency on extensive calibration data while maintaining accuracy.
    • * To overcome the limitations of traditional calibration techniques in complex scenarios.

    Main Methods:

    • * Implementation of a regularization neural network designed to directly map star vectors to star point coordinates.
    • * Incorporation of regularization strategies within the neural network structure and training algorithm to enhance generalization.
    • * Utilizing a multi-layer network architecture to model the complex relationship between star vectors and sensor outputs.

    Main Results:

    • * The proposed neural network method achieved high-precision calibration with significantly reduced data requirements.
    • * Simulation and experimental results confirmed the method's effectiveness, showing approximately a 30% reduction in calibration error compared to traditional approaches.
    • * The method demonstrated robust performance without relying on external a priori information.

    Conclusions:

    • * The regularization neural network offers a superior alternative for calibrating large FOV star sensors.
    • * The method effectively addresses complex distortions and error coupling issues inherent in star sensor calibration.
    • * This approach meets the stringent precision requirements for advanced aerospace applications using large FOV star sensors.