Abstract: Under dynamic conditions, the smearing effect of star spots on the image plane reduces centroid extraction accuracy, which has an impact on attitude estimation. To enhance the dynamic performance of the star sensor, we propose a multiplication extended Kalman filter (MEKF)-aided non-blind star image restoration algorithm based on the heterogeneous blur kernel. The proposed algorithm consists of three procedures. First, the MEKF is used to estimate the attitude and gyro drift to eliminate the measurement error of the star sensor and gyro drift. Second, the attitude predicted by MEKF is used, which provides initial conditions and accelerates the subsequent algorithm. Finally, a gyro-assisted heterogeneous blur kernel estimation algorithm is presented for restoring non-uniform and nonlinear motion-blurred star images. In contrast to existing dynamic star image deblurring algorithms, which focus mostly on image content, the proposed method emphasizes the cause of motion blur by fusing MEKF and a heterogeneous blur kernel. This leads to significantly enhanced robustness against noise and improved restoration accuracy. Simulation results demonstrate that the proposed method significantly outperforms existing techniques, improving centroid extraction accuracy by up to 59.64% and pointing accuracy across all axes by more than 78.94%.

基于异质模糊核的乘性扩展卡尔曼滤波辅助非盲星图像复原算法

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