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稀疏重构是压缩感知理论的核心内容之一,为了将稀疏重构有效地应用于星敏感器的压缩成像过程中,从星图图像误差和星点特征误差两个方面分析稀疏重构对星图的影响。在图像误差方面,利用峰值信噪比评价指标考察星图在不同重构算法、不同压缩比下的重构质量;在特征误差方面,从理论上分析稀疏重构对星点特征的影响机理,提出星点特征重构误差的评价指标,考察星点的质心、亮度和数量特征的重构误差。结果表明,在所选算法各压缩比下,星图相比一般图像能够获得更高的重构质量,重构星点能够在很大程度上保持可用于姿态确定的特征信息,结论保证了利用重构星图进行姿态计算的正确性,进一步验证了压缩感知理论在星敏感器中应用的可行性,为实现星敏感器的压缩成像提供了现实依据。
Sparse reconstruction is one of the core contents of compressive sensing theory. In order to apply sparse reconstruction to the compression imaging of star sensor effectively, this paper analyzes the effects of sparse reconstruction on star Effect of illustration. In terms of image error, the peak signal-to-noise ratio evaluation index is used to investigate the reconstruction quality of the star image under different reconstruction algorithms and different compression ratios. In the aspect of characteristic error, the mechanism of sparse reconstruction on the star feature is theoretically analyzed. The evaluation index of the reconstruction error of the star point feature is proposed, and the reconstruction error of the centroid, brightness and the number of the star point is investigated. The results show that under the compression ratio of the selected algorithm, the star image can obtain higher reconstruction quality than the general image, and the reconstructed star can largely maintain the characteristic information that can be used for attitude determination. The conclusion ensures that the use of the Reconstruction of the star map for the correctness of attitude calculation further verifies the feasibility of compressed sensing theory in star sensor and provides a realistic basis for the compression imaging of star sensor.