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Journal of Zhejiang University SCIENCE C 1998 Vol.-1 No.-1 P.

http://doi.org/10.1631/FITEE.2500193


Multiplication extended kalman filter-aided non-blind star image restoration algorithm based on the heterogeneous blur kernel


Author(s):  Yang LIU1, 2, 3, Huajian DENG1, Hao WANG1, 2, 3, Zhonghe JIN1, 2, 3

Affiliation(s):  1Micro-Satellite Research Center, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   12224002@zju.edu.cn, denghuajian@zju.edu.cn, roger@zju.edu.cn, jinzh@zju.edu.cn

Key Words:  Heterogeneous blur kernel estimation, Dynamic conditions, Regional image restoration, Multiplication extended Kalman filter, Gyro drift


Yang LIU1,2,3, Huajian DENG1, Hao WANG1,2,3, Zhonghe JIN1,2,3. Multiplication extended kalman filter-aided non-blind star image restoration algorithm based on the heterogeneous blur kernel[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .

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author="Yang LIU1,2,3, Huajian DENG1, Hao WANG1,2,3, Zhonghe JIN1,2,3",
journal="Frontiers of Information Technology & Electronic Engineering",
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year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2500193"
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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, 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 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%.

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