Abstract

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

Full Article  |  PDF Article
OSA Recommended Articles
Region-confined restoration method for motion-blurred star image of the star sensor under dynamic conditions

Liheng Ma, Franco Bernelli-Zazzera, Guangwen Jiang, Xingshu Wang, Zongsheng Huang, and Shiqiao Qin
Appl. Opt. 55(17) 4621-4631 (2016)

Smearing model and restoration of star image under conditions of variable angular velocity and long exposure time

Ting Sun, Fei Xing, Zheng You, Xiaochu Wang, and Bin Li
Opt. Express 22(5) 6009-6024 (2014)

Effective star tracking method based on optical flow analysis for star trackers

Ting Sun, Fei Xing, Xiaochu Wang, Jin Li, Minsong Wei, and Zheng You
Appl. Opt. 55(36) 10335-10340 (2016)

More Recommended Articles

References

  • View by:
  • Article Order
  • |
  • Year
  • |
  • Author
  • |
  • Publication
  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. SPIE 2350, 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]

2013 (1)

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]

2012 (1)

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]

2007 (1)

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]

2004 (1)

S. Zheng, Y. Tian, J. Tian, and J. Liu, “Facet-based star acquisition method,” Opt. Eng. 43(11), 2796–2805 (2004).
[Crossref]

2003 (1)

G. Rufino and D. Accardo, “Enhancement of the centroiding algorithm for star tracker measure refinement,” Acta Astronaut. 53(2), 135–147 (2003).
[Crossref]

2002 (1)

C. C. Liebe, “Accuracy performance of star trackers-A tutorial,” IEEE Trans. Aerosp. Electron. Syst. 38(2), 587–599 (2002).
[Crossref]

2000 (2)

J. A. Stark, “Adaptive image contrast enhancement using generalizations of histogram equalization,” IEEE Trans. Image Process. 9(5), 889–896 (2000).
[Crossref] [PubMed]

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]

1999 (3)

L. L. Kontsevich and C. W. Tyler, “Bayesian adaptive estimation of psychometric slope and threshold,” Vision Res. 39(16), 2729–2737 (1999).
[Crossref] [PubMed]

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]

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]

1997 (1)

Y. Yitzhaky and N. S. Kopeika, “Identification of blur parameters from motion blurred images,” Graphical Models Image Process. 59(5), 310–320 (1997).
[Crossref]

1994 (1)

M. R. Shortis, T. A. Clarke, and T. Short, “A comparison of some techniques for the subpixel location of discrete target image,” Proc. SPIE 2350, 239–250 (1994).
[Crossref]

1993 (1)

A. E. Savakis and H. J. Trussell, “Blur identification by residual spectral matching,” IEEE Trans. Image Process. 2(2), 141–151 (1993).
[Crossref] [PubMed]

1991 (1)

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]

1990 (1)

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]

1979 (1)

N. OTSU, “A Threshold Selection Method from Gray-Level Histograms,” IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979).
[Crossref]

1976 (1)

M. Cannon, “Blind deconvolution of spatially invariant image blurs with phase,” IEEE Trans. Acoust. Speech Signal Process. 24(1), 58–63 (1976).
[Crossref]

1965 (1)

G. Wahba, “A least squares estimate of satellite attitude,” SIAM Rev. 7(3), 409–409 (1965).
[Crossref]

Accardo, D.

G. Rufino and D. Accardo, “Enhancement of the centroiding algorithm for star tracker measure refinement,” Acta Astronaut. 53(2), 135–147 (2003).
[Crossref]

Bernsen, J.

J. Bernsen, “Dynamic thresholding of grey-level images,” in Proceedings 8th International Conference on Pattern Recognition, Paris, pp. 1251–1255, 1986.

Biemond, J.

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]

Cannon, M.

M. Cannon, “Blind deconvolution of spatially invariant image blurs with phase,” IEEE Trans. Acoust. Speech Signal Process. 24(1), 58–63 (1976).
[Crossref]

Chang, S. G.

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]

Clarke, T. A.

M. R. Shortis, T. A. Clarke, and T. Short, “A comparison of some techniques for the subpixel location of discrete target image,” Proc. SPIE 2350, 239–250 (1994).
[Crossref]

Guo, L.

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]

Hornsey, R.

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]

Ko, S. J.

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]

Kontsevich, L. L.

L. L. Kontsevich and C. W. Tyler, “Bayesian adaptive estimation of psychometric slope and threshold,” Vision Res. 39(16), 2729–2737 (1999).
[Crossref] [PubMed]

Kopeika, N. S.

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]

Y. Yitzhaky and N. S. Kopeika, “Identification of blur parameters from motion blurred images,” Graphical Models Image Process. 59(5), 310–320 (1997).
[Crossref]

Lagendijk, R. L.

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]

Lee, Y. H.

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]

Liebe, C. C.

C. C. Liebe, “Accuracy performance of star trackers-A tutorial,” IEEE Trans. Aerosp. Electron. Syst. 38(2), 587–599 (2002).
[Crossref]

Liu, J.

S. Zheng, Y. Tian, J. Tian, and J. Liu, “Facet-based star acquisition method,” Opt. Eng. 43(11), 2796–2805 (2004).
[Crossref]

Mebrahtu, H.

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]

Milberg, R.

Quan, W.

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]

Quine, B. M.

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]

Rufino, G.

G. Rufino and D. Accardo, “Enhancement of the centroiding algorithm for star tracker measure refinement,” Acta Astronaut. 53(2), 135–147 (2003).
[Crossref]

Savakis, A. E.

A. E. Savakis and H. J. Trussell, “Blur identification by residual spectral matching,” IEEE Trans. Image Process. 2(2), 141–151 (1993).
[Crossref] [PubMed]

Short, T.

M. R. Shortis, T. A. Clarke, and T. Short, “A comparison of some techniques for the subpixel location of discrete target image,” Proc. SPIE 2350, 239–250 (1994).
[Crossref]

Shortis, M. R.

M. R. Shortis, T. A. Clarke, and T. Short, “A comparison of some techniques for the subpixel location of discrete target image,” Proc. SPIE 2350, 239–250 (1994).
[Crossref]

Stark, J. A.

J. A. Stark, “Adaptive image contrast enhancement using generalizations of histogram equalization,” IEEE Trans. Image Process. 9(5), 889–896 (2000).
[Crossref] [PubMed]

Sun, T.

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]

Tarasyuk, V.

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]

Tekalp, A. M.

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]

Tian, J.

S. Zheng, Y. Tian, J. Tian, and J. Liu, “Facet-based star acquisition method,” Opt. Eng. 43(11), 2796–2805 (2004).
[Crossref]

Tian, Y.

S. Zheng, Y. Tian, J. Tian, and J. Liu, “Facet-based star acquisition method,” Opt. Eng. 43(11), 2796–2805 (2004).
[Crossref]

Trussell, H. J.

A. E. Savakis and H. J. Trussell, “Blur identification by residual spectral matching,” IEEE Trans. Image Process. 2(2), 141–151 (1993).
[Crossref] [PubMed]

Tyler, C. W.

L. L. Kontsevich and C. W. Tyler, “Bayesian adaptive estimation of psychometric slope and threshold,” Vision Res. 39(16), 2729–2737 (1999).
[Crossref] [PubMed]

Vetterli, M.

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]

Wahba, G.

G. Wahba, “A least squares estimate of satellite attitude,” SIAM Rev. 7(3), 409–409 (1965).
[Crossref]

Wang, Z.

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]

Xing, F.

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]

Yitzhaky, Y.

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]

Y. Yitzhaky and N. S. Kopeika, “Identification of blur parameters from motion blurred images,” Graphical Models Image Process. 59(5), 310–320 (1997).
[Crossref]

Yohaev, S.

You, Z.

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]

Yu, B.

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]

Zhang, D.

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]

Zhang, W.

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]

Zheng, S.

S. Zheng, Y. Tian, J. Tian, and J. Liu, “Facet-based star acquisition method,” Opt. Eng. 43(11), 2796–2805 (2004).
[Crossref]

Acta Astronaut. (1)

G. Rufino and D. Accardo, “Enhancement of the centroiding algorithm for star tracker measure refinement,” Acta Astronaut. 53(2), 135–147 (2003).
[Crossref]

Appl. Opt. (1)

Comput. Phys. Commun. (1)

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]

Graphical Models Image Process. (1)

Y. Yitzhaky and N. S. Kopeika, “Identification of blur parameters from motion blurred images,” Graphical Models Image Process. 59(5), 310–320 (1997).
[Crossref]

IEEE Trans. Acoust. Speech Signal Process. (1)

M. Cannon, “Blind deconvolution of spatially invariant image blurs with phase,” IEEE Trans. Acoust. Speech Signal Process. 24(1), 58–63 (1976).
[Crossref]

IEEE Trans. Aerosp. Electron. Syst. (1)

C. C. Liebe, “Accuracy performance of star trackers-A tutorial,” IEEE Trans. Aerosp. Electron. Syst. 38(2), 587–599 (2002).
[Crossref]

IEEE Trans. Circ. Syst. (1)

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]

IEEE Trans. Circuits Syst. II-Analog Digital Sig. Process. (1)

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]

IEEE Trans. Image Process. (3)

J. A. Stark, “Adaptive image contrast enhancement using generalizations of histogram equalization,” IEEE Trans. Image Process. 9(5), 889–896 (2000).
[Crossref] [PubMed]

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]

A. E. Savakis and H. J. Trussell, “Blur identification by residual spectral matching,” IEEE Trans. Image Process. 2(2), 141–151 (1993).
[Crossref] [PubMed]

IEEE Trans. Syst. Man Cybern. (1)

N. OTSU, “A Threshold Selection Method from Gray-Level Histograms,” IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979).
[Crossref]

Opt. Eng. (2)

S. Zheng, Y. Tian, J. Tian, and J. Liu, “Facet-based star acquisition method,” Opt. Eng. 43(11), 2796–2805 (2004).
[Crossref]

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]

Proc. SPIE (1)

M. R. Shortis, T. A. Clarke, and T. Short, “A comparison of some techniques for the subpixel location of discrete target image,” Proc. SPIE 2350, 239–250 (1994).
[Crossref]

Sensors (Basel) (2)

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]

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]

SIAM Rev. (1)

G. Wahba, “A least squares estimate of satellite attitude,” SIAM Rev. 7(3), 409–409 (1965).
[Crossref]

Vision Res. (1)

L. L. Kontsevich and C. W. Tyler, “Bayesian adaptive estimation of psychometric slope and threshold,” Vision Res. 39(16), 2729–2737 (1999).
[Crossref] [PubMed]

Other (7)

A. K. Katsaggelos, Digital Image Restoration (Springer-Verlag, 1991).

J. Gwanghyeok, Autonomous star sensing, pattern identification, and attitude determination for spacecraft: an analytical and experiment study Doctoral thesis, Texas A&M University, 2001.

M. A. Samaan, Toward faster and more accurate star sensors using recursive centroiding and star identification Doctoral thesis, Texas A&M University, 2003.

D. S. Anderson, Autonomous star sensing and pattern recognition for spacecraft attitude determination Doctoral thesis, Texas A&M University, 1991.

R. C. Gonzalez, Digital Image Processing (Pearson Education, 2009).

A. B. Katake, Modeling, image processing and attitude estimation of high speed star sensors Doctoral thesis, Texas A&M University, 2006.

J. Bernsen, “Dynamic thresholding of grey-level images,” in Proceedings 8th International Conference on Pattern Recognition, Paris, pp. 1251–1255, 1986.

Cited By

OSA participates in Crossref's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (13)

Fig. 1
Fig. 1

The entire star acquisition procedure.

Download Full Size | PPT Slide | PDF

Fig. 2
Fig. 2

Sketch map of open operation.

Download Full Size | PPT Slide | PDF

Fig. 3
Fig. 3

The schematic of star extraction and centroid determination with reference window for one star region.

Download Full Size | PPT Slide | PDF

Fig. 4
Fig. 4

Star extraction and recognition results without our method in this paper (a); (b), (c), (d) are gray scale, colour scale and 3D plots of the same detailed view of one star point.

Download Full Size | PPT Slide | PDF

Fig. 5
Fig. 5

Star extraction and recognition result with our method (a); (b), (c), (d) are gray scale, color scale and 3D plots of the same detailed view of one star point.

Download Full Size | PPT Slide | PDF

Fig. 6
Fig. 6

Motion direction identification result with long Step of 10°. (a) displays the results of all identified stars; (b) is the summation of (a).

Download Full Size | PPT Slide | PDF

Fig. 7
Fig. 7

Motion direction identification result with precise Step of 1°. (a) displays the results of all identified stars;(b) is the summation of (a).

Download Full Size | PPT Slide | PDF

Fig. 8
Fig. 8

Autocorrelation curve of differential. (a) displays the result of arbitrarily chosen star point with ID 1527; (b) displays the result of arbitrarily chosen star point with ID 1474.

Download Full Size | PPT Slide | PDF

Fig. 9
Fig. 9

Laboratory experiment system.

Download Full Size | PPT Slide | PDF

Fig. 10
Fig. 10

Gray image of bright spot with 0.4°/s motion speed in vertical direction. (a) is the original image; (b) is the processed image using correlation filter and background removing proposed in this work.

Download Full Size | PPT Slide | PDF

Fig. 11
Fig. 11

Motion direction identification result with precise Step of 10°.

Download Full Size | PPT Slide | PDF

Fig. 12
Fig. 12

Autocorrelation curve of differential.

Download Full Size | PPT Slide | PDF

Fig. 13
Fig. 13

Analysis result at 1.2°/s motion speed in vertical direction. (a) is the original image; (b) is the processed image using correlation filter and background removing proposed in this work; (c) is Motion direction identification result with precise Step of 10°; (d) is Autocorrelation curve of differential.

Download Full Size | PPT Slide | PDF

Tables (3)

Tables Icon

Table 1 Image Information

View Table | View all tables in this article
Tables Icon

Table 2 Information on Recognized Star Points

View Table | View all tables in this article
Tables Icon

Table 3 Accuracy of Star Point Positions with Different Processing Methods

View Table | View all tables in this article

Equations (15)

Equations on this page are rendered with MathJax. Learn more.

c(t)=f(t)∘h(t)= ∫ −∞ ∞ f(τ)h(τ−t)dτ .
c(x,y)=f(x,y)∘h(x,y)= ∑ m=0 M−1 ∑ n=0 N−1 f(m,n)h(m−x,n−y) ,
C filter = F org ∘ H filter .
t1(x,y)=cΘb(x,y)= min (i,j)∈ D b (x+i,y+j)∈ D c [c(x+i,y+j)−b(i,j)].
t2(x,y)=c⊕b(x,y)= max (i,j)∈ D b (x−i,y−j)∈ D c [c(x−i,y−j)+b(i,j)].
B(x,y)= 1 2(K−1)+1 ⋅ 1 2(L−1)+1 ⋅ ∑ i=−(K−1) K−1 ∑ j=−(L−1) L−1 t(x+i,y+j) .
T(x,y)=B(x,y)+σ,
g(x,y)=c(x,y)−B(x,y).
Δg (i,j) α =g' (u+1,v) 0 −g' (u,v) 0 .
I (Δg) α = ∑ i=0 2 ∑ j=0 2 | Δg (i,j) α | .
α ⌢ =angular(min(I (Δg) α )).
∇ soble =[ 1 0 −1 2 0 −2 1 0 −1 ].
χ' (i,j) 0 =χ (i,j) 0 ∗ ∇ soble ,
S(i,j)= ∑ n=0 N−1 χ' (i,n) 0 χ' (i,n+j) 0 ,
S sum = ∑ i=0 M−1 S(i,j) ,

Metrics