OSA's Digital Library

Optics Express

Optics Express

  • Editor: Andrew M. Weiner
  • Vol. 21, Iss. 17 — Aug. 26, 2013
  • pp: 20096–20110

Motion-blurred star acquisition method of the star tracker under high dynamic conditions

Ting Sun, Fei Xing, Zheng You, and Minsong Wei  »View Author Affiliations

Optics Express, Vol. 21, Issue 17, pp. 20096-20110 (2013)

View Full Text Article

Enhanced HTML    Acrobat PDF (2234 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



The star tracker is one of the most promising attitude measurement devices used in spacecraft due to its extremely high accuracy. However, high dynamic performance is still one of its constraints. Smearing appears, making it more difficult to distinguish the energy dispersive star point from the noise. An effective star acquisition approach for motion-blurred star image is proposed in this work. The correlation filter and mathematical morphology algorithm is combined to enhance the signal energy and evaluate slowly varying background noise. The star point can be separated from most types of noise in this manner, making extraction and recognition easier. Partial image differentiation is then utilized to obtain the motion parameters from only one image of the star tracker based on the above process. Considering the motion model, the reference window is adopted to perform centroid determination. Star acquisition results of real on-orbit star images and laboratory validation experiments demonstrate that the method described in this work is effective and the dynamic performance of the star tracker could be improved along with more identified stars and guaranteed position accuracy of the star point.

© 2013 OSA

OCIS Codes
(100.2960) Image processing : Image analysis
(120.4640) Instrumentation, measurement, and metrology : Optical instruments
(100.4145) Image processing : Motion, hyperspectral image processing
(120.6085) Instrumentation, measurement, and metrology : Space instrumentation

ToC Category:
Instrumentation, Measurement, and Metrology

Original Manuscript: July 22, 2013
Revised Manuscript: August 4, 2013
Manuscript Accepted: August 9, 2013
Published: August 19, 2013

Ting Sun, Fei Xing, Zheng You, and Minsong Wei, "Motion-blurred star acquisition method of the star tracker under high dynamic conditions," Opt. Express 21, 20096-20110 (2013)

Sort:  Author  |  Year  |  Journal  |  Reset  


  1. C. C. Liebe, “Accuracy performance of star trackers-A tutorial,” IEEE Trans. Aerosp. Electron. Syst.38(2), 587–599 (2002). [CrossRef]
  2. J. Gwanghyeok, Autonomous star sensing, pattern identification, and attitude determination for spacecraft: an analytical and experiment study Doctoral thesis, Texas A&M University, 2001.
  3. T. Sun, F. Xing, and Z. You, “Optical system error analysis and calibration method of high-accuracy star trackers,” Sensors (Basel)13(4), 4598–4623 (2013). [CrossRef] [PubMed]
  4. B. M. Quine, V. Tarasyuk, H. Mebrahtu, and R. Hornsey, “Determining star-image location: A new sub-pixel interpolation technique to process image centroids,” Comput. Phys. Commun.177(9), 700–706 (2007). [CrossRef]
  5. G. Rufino and D. Accardo, “Enhancement of the centroiding algorithm for star tracker measure refinement,” Acta Astronaut.53(2), 135–147 (2003). [CrossRef]
  6. D. S. Anderson, Autonomous star sensing and pattern recognition for spacecraft attitude determination Doctoral thesis, Texas A&M University, 1991.
  7. M. R. Shortis, T. A. Clarke, and T. Short, “A comparison of some techniques for the subpixel location of discrete target image,” Proc. SPIE2350, 239–250 (1994). [CrossRef]
  8. M. A. Samaan, Toward faster and more accurate star sensors using recursive centroiding and star identification Doctoral thesis, Texas A&M University, 2003.
  9. S. Zheng, Y. Tian, J. Tian, and J. Liu, “Facet-based star acquisition method,” Opt. Eng.43(11), 2796–2805 (2004). [CrossRef]
  10. R. C. Gonzalez, Digital Image Processing (Pearson Education, 2009).
  11. S. J. Ko and Y. H. Lee, “Center weighted median filters and their applications to image enhancement,” IEEE Trans. Circ. Syst.38(9), 984–993 (1991). [CrossRef]
  12. Z. Wang and D. Zhang, “Progressive switching median filter for the removal of impulse noise from highly corrupted images,” IEEE Trans. Circuits Syst. II-Analog Digital Sig. Process.46(1), 78–80 (1999). [CrossRef]
  13. J. A. Stark, “Adaptive image contrast enhancement using generalizations of histogram equalization,” IEEE Trans. Image Process.9(5), 889–896 (2000). [CrossRef] [PubMed]
  14. N. OTSU, “A Threshold Selection Method from Gray-Level Histograms,” IEEE Trans. Syst. Man Cybern.9(1), 62–66 (1979). [CrossRef]
  15. J. Bernsen, “Dynamic thresholding of grey-level images,” in Proceedings 8th International Conference on Pattern Recognition, Paris, pp. 1251–1255, 1986.
  16. L. L. Kontsevich and C. W. Tyler, “Bayesian adaptive estimation of psychometric slope and threshold,” Vision Res.39(16), 2729–2737 (1999). [CrossRef] [PubMed]
  17. S. G. Chang, B. Yu, and M. Vetterli, “Adaptive wavelet thresholding for image denoising and compression,” IEEE Trans. Image Process.9(9), 1532–1546 (2000). [CrossRef] [PubMed]
  18. A. B. Katake, Modeling, image processing and attitude estimation of high speed star sensors Doctoral thesis, Texas A&M University, 2006.
  19. W. Zhang, W. Quan, and L. Guo, “Blurred star image processing for star sensors under dynamic conditions,” Sensors (Basel)12(5), 6712–6726 (2012). [CrossRef] [PubMed]
  20. M. Cannon, “Blind deconvolution of spatially invariant image blurs with phase,” IEEE Trans. Acoust. Speech Signal Process.24(1), 58–63 (1976). [CrossRef]
  21. A. K. Katsaggelos, Digital Image Restoration (Springer-Verlag, 1991).
  22. J. Biemond, A. M. Tekalp, and R. L. Lagendijk, “Maximum likelihood image and blur identification: A unifying approach,” Opt. Eng.29(5), 422–435 (1990). [CrossRef]
  23. A. E. Savakis and H. J. Trussell, “Blur identification by residual spectral matching,” IEEE Trans. Image Process.2(2), 141–151 (1993). [CrossRef] [PubMed]
  24. Y. Yitzhaky and N. S. Kopeika, “Identification of blur parameters from motion blurred images,” Graphical Models Image Process.59(5), 310–320 (1997). [CrossRef]
  25. Y. Yitzhaky, R. Milberg, S. Yohaev, and N. S. Kopeika, “Comparison of direct blind deconvolution methods for motion-blurred images,” Appl. Opt.38(20), 4325–4332 (1999). [CrossRef] [PubMed]
  26. G. Wahba, “A least squares estimate of satellite attitude,” SIAM Rev.7(3), 409–409 (1965). [CrossRef]

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited