压缩感知稀疏重构对星图的影响

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。

Effect of compressive sensing sparse reconstruction on star image

The sparse reconstruction is one of the key factors in compressive sensing theory. In order to apply sparse reconstruction to compressive imaging process of star tracker in an effective way, this paper analyzes the effect of sparse reconstruction on star image with respect to star image error and star feature error. In terms of image error, PSNR evaluation index is employed to observe reconstruction quality of star image under different reconstruction algorithm and diverse compression ratio. While in terms of feature error, firstly, the influential mechanism of sparse reconstruction on star feature is analyzed in theory; and then the evaluation index of reconstruction error of star feature is proposed to investigate the reconstruction error of the star centroid, brightness and number. The result shows that, under diverse compression ratio of selected algorithm, the star image can obtain higher reconstruction quality than the common image under the same reconstruction condition, and the reconstructed star can keep its features for attitude determination to a large extent. The conclusion proves the validity of attitude estimation using the reconstructed star image, verifies the feasibility of applying compressive sensing to star tracker and provides the practical foundation for compressive imaging of star tracker.