1 April 2015 Imaging method with low data rate for autonomous star trackers based on compressive sensing
Xin Song, Hang Yin, Ye Yan
Author Affiliations +
Abstract
Compressive sensing (CS) theory is popular and has led to the development of new imaging methods in many fields. Specifically, CS theory can reduce the sampling rate of a high-accuracy star tracker and maintain the quality of star images. However, the imaging system of a star tracker contains certain properties that cannot correspond to conventional measurement architectures. Thus, a compressive imaging system that is suitable for the star trackers used in space missions is proposed. First, the CS framework is briefly introduced and the feasibility of typical measurement architectures is analyzed. Second, an imaging method with a low data rate and based on deterministic phase modulation (DPM) is presented to form a weight block circulant matrix (WBCM) of deterministic measurement. The advantage of the proposed imaging method is determined by comparing it with the typical measurement architectures. Third, numerical simulations are conducted to investigate the performance of the proposed method. Results show that the reconstruction quality of the DPM architecture is close to that of random phase modulation architecture. Meanwhile, the WBCM measurement is better than the conventional block circulant matrix measurement. Furthermore, the statistical result significantly ensures accurate star centroid estimation and confirms the feasibility of CS application to star tracker imaging at low data rates.
© 2015 SPIE and IS&T 1017-9909/2015/$25.00 © 2015 SPIE and IS&T
Xin Song, Hang Yin, and Ye Yan "Imaging method with low data rate for autonomous star trackers based on compressive sensing," Journal of Electronic Imaging 24(2), 023024 (1 April 2015). https://doi.org/10.1117/1.JEI.24.2.023024
Published: 1 April 2015
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CITATIONS
Cited by 4 scholarly publications.
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KEYWORDS
Stars

Phase modulation

Imaging systems

Content addressable memory

Modulation

Reconstruction algorithms

Compressed sensing

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