Ms. Yu Duan Profile

Yu Duan

at National Univ of Defense Technology

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Proceedings Article | 18 October 2016 Paper

A star identification algorithm for large FOV observations

Yu Duan, Zhaodong Niu, Zengping Chen

Proceedings Volume 10004, 100041G (2016) https://doi.org/10.1117/12.2240908

KEYWORDS: Stars, Sensors, Detection and tracking algorithms, Target detection, Distortion, Image sensors, Telescopes, Astronomy, CCD image sensors, Evolutionary algorithms

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Due to the broader extent of observation and higher detection probability of space targets, large FOV (field of vision) optical instruments are widely used in astronomical applications.. However, the high density of observed stars and the distortion of the optical system often bring about inaccuracy in star locations. So in large FOV observations, many conventional star identification algorithms do not show very good performance. In this paper, we propose a star identification method with a low requirement for observation accuracy and thus suitable for large FOV circumstances. The proposed method includes two stages. The former is based on the match group algorithm, in addition to which we exploit the information of differential angles of inclination for verification. The inclinations of satellite stars are computed by reference to the selected pole stars. Then we obtain a set of identified stars for further recognition. The latter stage involves four steps. First, we derive the relationship between the rectangular coordinates of catalog stars and sensor stars with the identified locations obtained. Second, we transform the sensor coordinates to the catalog coordinates and find the catalog stars at close range as candidates. Third, we calculate the angle of inclination of each unidentified sensor star in relation to the nearest previously identified one, and the angular separation between them as well, to compare with those of the candidates. At last, candidates satisfying the limitations are considered the appropriate correspondences. The experimental results show that in large FOV observations, the proposed method presents better performance in comparison with several typical star identification methods in open literature.

SPIE Journal Paper | 6 September 2016

Star smear removal for full-frame charge-coupled device images based on Gaussian fitting

Yu Duan, Peiliang Jing, Zhaodong Niu, Zengping Chen

JARS, Vol. 10, Issue 03, 035019, (September 2016) https://doi.org/10.1117/12.10.1117/1.JRS.10.035019

KEYWORDS: Stars, Charge-coupled devices, Signal to noise ratio, Image processing, CCD image sensors, Detection and tracking algorithms, Sensors, Target detection, Visualization, Defense technologies

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Image smear, produced by the shutter-less operation of full-frame charge-coupled device (CCD) sensors, greatly affects the performance of target detection, the centering accuracy, and visual magnitude estimation. We study the operation principle of full-frame CCDs, analyze the cause and properties of smear effect, and propose a smear removal algorithm for star images of full-frame CCDs. The proposed method locates the smears and extracts the rough profiles of the smeared stars by finding the conditional extrema. Then Gaussian fitting is applied to accurately extract the stars, in order to maintain the integrity of star images while minimizing the smear effect. The extraction of smears and stars requires parameters such as the size of the CCD, the integration time and the readout time, as well as the estimation of background noise. We assess the performance of our scheme with real observed data. The experimental results show that the proposed scheme improves the average signal-to-noise ratio of the images by about 22%, presenting better smear removal performance compared with several published methods. The limitation of the proposed algorithm includes the difficulty of distinguishing between two very close stars displaying the gray level of a single peak and overestimation of the background noise may also influence the performance of the algorithm.

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