Abstract

The star tracker plays a critical role in precision aerospace missions due to its high accuracy, absolute attitude output, and low power consumption. For an optical sensor, the problem of stray light is always an important research issue. A star energy information mining method for stray light suppression is proposed in this study. The gray-level co-occurrence matrix and k-nearest neighbor algorithm are adopted to identify the types of stray light that enter the optical system. Effective recognition of the stray light types is an important premise for the following steps. Then the parameters are optimized during background estimation. When star spots are extracted, the local differential encoding combined with Levenshtein distance filtering is conducted to eliminate the interference noise spots. The proposed algorithm can achieve accurate star spot extraction even when stray light exists in real night sky observation experiments.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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References

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  1. C. C. Liebe, “Accuracy performance of star trackers—a tutorial,” IEEE Trans. Aerosp. Electron. Syst. 38, 587–599 (2002).
    [Crossref]
  2. D. A. Glenar, T. J. Stubbs, J. M. Hahn, and Y. Wang, “Search for a high-altitude lunar dust exosphere using Clementine navigational star tracker measurements,” J. Geophys. Res. Planets 119, 2548–2567 (2014).
    [Crossref]
  3. N. S. Wolfenbarger, “Characterization of star tracker distortion for the ICESat mission,” Doctoral dissertation (The University of Texas, 2017).
  4. I. Jovanovic and J. Enright, “Towards star tracker geolocation for planetary navigation,” in Proceedings of IEEE Aerospace Conference (2017), pp. 1–6
  5. P. Jayaraman, J. Fischer, A. Moorhouse, and M. Lauer, “Star tracker operational usage in different phases of the Mars Express mission,” in Proceedings of SpaceOps 2006 Conference (2013).
  6. M. Wei, F. Xing, and Z. You, “A real-time detection and positioning method for small and weak targets using a 1D morphology-based approach in 2D images,” Light Sci. Appl. 7, 18006 (2018).
    [Crossref]
  7. T. Sun, F. Xing, Z. You, X. Wang, and B. Li, “Smearing model and restoration of star image under conditions of variable angular velocity and long exposure time,” Opt. Express 22, 6009–6024 (2014).
    [Crossref]
  8. J. Yan, J. Jiang, and G. Zhang, “Modeling of intensified high dynamic star tracker,” Opt. Express 25, 927–948 (2017).
    [Crossref]
  9. T. Sun, F. Xing, X. Wang, Z. You, and D. Chu, “An accuracy measurement method for star trackers based on direct astronomic observation,” Sci. Rep. 6, 22593 (2016).
    [Crossref]
  10. S. Zhang, F. Xing, T. Sun, Z. You, and M. Wei, “Novel approach to improve the attitude update rate of a star tracker,” Opt. Express 26, 5164–5181 (2018).
    [Crossref]
  11. T. Sun, F. Xing, X. Wang, J. Li, M. Wei, and Z. You, “Effective star tracking method based on optical flow analysis for star trackers,” Appl. Opt. 55, 10335–10340 (2016).
    [Crossref]
  12. G. Wang, F. Xing, M. Wei, T. Sun, and Z. You, “Optimization method of star tracker orientation for sun-synchronous orbit based on space light distribution,” Appl. Opt. 56, 4480–4490 (2017).
    [Crossref]
  13. J. Fang and X. Ning, “Installation direction analysis of star sensors by hybrid condition number,” IEEE Trans. Instrum. Meas. 58, 3576–3582 (2009).
    [Crossref]
  14. G. Wang, F. Xing, M. Wei, T. Sun, and Z. You, “Optimization method for star tracker orientation in the sun-pointing mode,” Chin. Opt. Lett. 15, 081201 (2017).
    [Crossref]
  15. M. Berman and M. Munur, “Validating microsatellite star tracker baffle tests,” in Proceedings of AIAA/AAS Astrodynamics Specialist Conference (2013).
  16. T. Sun, F. Xing, Z. You, X. Wang, and B. Li, “Deep coupling of star tracker and MEMS-gyro data under highly dynamic and long exposure conditions,” Meas. Sci. Technol. 25, 085003 (2014).
    [Crossref]
  17. J. E. Potter and W. W. E. Vande, “Optimum mixing of gyroscope and star tracker data,” J. Spacecr. Rockets 5, 536–540 (1968).
    [Crossref]
  18. B. O. Hua, M. A. Fu-Long, and L. C. Jiao, “Research on computation of GLCM of image texture,” Acta Electron. Sin. 1, 155–158 (2006).
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    [Crossref]
  20. F. R. D. Siqueira, W. R. Schwartz, and H. Pedrini, “Multi-scale gray level co-occurrence matrices for texture description,” Neurocomputing 120, 336–345 (2013).
    [Crossref]
  21. K. Q. Weinberger and L. K. Saul, “Distance metric learning for large margin nearest neighbor classification,” J. Mach. Learn. Res. 10, 207–244 (2009).
  22. S. J. Smith, M. O. Bourgoin, K. Sims, and H. L. Voorhees, “Handwritten character classification using nearest neighbor in large databases,” IEEE Trans. Pattern Anal. Mach. Intell. 16, 915–919 (1994).
    [Crossref]
  23. T. Sun, F. Xing, Z. You, and M. Wei, “Motion-blurred star acquisition method of the star tracker under high dynamic conditions,” Opt. Express 21, 20096–20110 (2013).
    [Crossref]
  24. E. S. Ristad and P. N. Yianilos, “Learning string-edit distance,” IEEE Trans. Pattern Anal. Mach. Intell. 20, 522–532 (1998).
    [Crossref]
  25. P. Bille, “A survey on tree edit distance and related problems,” Theor. Comput. Sci. 337, 217–239 (2005).
    [Crossref]

2018 (2)

M. Wei, F. Xing, and Z. You, “A real-time detection and positioning method for small and weak targets using a 1D morphology-based approach in 2D images,” Light Sci. Appl. 7, 18006 (2018).
[Crossref]

S. Zhang, F. Xing, T. Sun, Z. You, and M. Wei, “Novel approach to improve the attitude update rate of a star tracker,” Opt. Express 26, 5164–5181 (2018).
[Crossref]

2017 (3)

2016 (2)

T. Sun, F. Xing, X. Wang, Z. You, and D. Chu, “An accuracy measurement method for star trackers based on direct astronomic observation,” Sci. Rep. 6, 22593 (2016).
[Crossref]

T. Sun, F. Xing, X. Wang, J. Li, M. Wei, and Z. You, “Effective star tracking method based on optical flow analysis for star trackers,” Appl. Opt. 55, 10335–10340 (2016).
[Crossref]

2014 (3)

T. Sun, F. Xing, Z. You, X. Wang, and B. Li, “Smearing model and restoration of star image under conditions of variable angular velocity and long exposure time,” Opt. Express 22, 6009–6024 (2014).
[Crossref]

T. Sun, F. Xing, Z. You, X. Wang, and B. Li, “Deep coupling of star tracker and MEMS-gyro data under highly dynamic and long exposure conditions,” Meas. Sci. Technol. 25, 085003 (2014).
[Crossref]

D. A. Glenar, T. J. Stubbs, J. M. Hahn, and Y. Wang, “Search for a high-altitude lunar dust exosphere using Clementine navigational star tracker measurements,” J. Geophys. Res. Planets 119, 2548–2567 (2014).
[Crossref]

2013 (2)

T. Sun, F. Xing, Z. You, and M. Wei, “Motion-blurred star acquisition method of the star tracker under high dynamic conditions,” Opt. Express 21, 20096–20110 (2013).
[Crossref]

F. R. D. Siqueira, W. R. Schwartz, and H. Pedrini, “Multi-scale gray level co-occurrence matrices for texture description,” Neurocomputing 120, 336–345 (2013).
[Crossref]

2009 (2)

K. Q. Weinberger and L. K. Saul, “Distance metric learning for large margin nearest neighbor classification,” J. Mach. Learn. Res. 10, 207–244 (2009).

J. Fang and X. Ning, “Installation direction analysis of star sensors by hybrid condition number,” IEEE Trans. Instrum. Meas. 58, 3576–3582 (2009).
[Crossref]

2006 (1)

B. O. Hua, M. A. Fu-Long, and L. C. Jiao, “Research on computation of GLCM of image texture,” Acta Electron. Sin. 1, 155–158 (2006).

2005 (1)

P. Bille, “A survey on tree edit distance and related problems,” Theor. Comput. Sci. 337, 217–239 (2005).
[Crossref]

2002 (1)

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

1999 (1)

L. K. Soh and C. Tsatsoulis, “Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices,” IEEE Trans. Geosci. Remote Sens. 37, 780–795 (1999).
[Crossref]

1998 (1)

E. S. Ristad and P. N. Yianilos, “Learning string-edit distance,” IEEE Trans. Pattern Anal. Mach. Intell. 20, 522–532 (1998).
[Crossref]

1994 (1)

S. J. Smith, M. O. Bourgoin, K. Sims, and H. L. Voorhees, “Handwritten character classification using nearest neighbor in large databases,” IEEE Trans. Pattern Anal. Mach. Intell. 16, 915–919 (1994).
[Crossref]

1968 (1)

J. E. Potter and W. W. E. Vande, “Optimum mixing of gyroscope and star tracker data,” J. Spacecr. Rockets 5, 536–540 (1968).
[Crossref]

Berman, M.

M. Berman and M. Munur, “Validating microsatellite star tracker baffle tests,” in Proceedings of AIAA/AAS Astrodynamics Specialist Conference (2013).

Bille, P.

P. Bille, “A survey on tree edit distance and related problems,” Theor. Comput. Sci. 337, 217–239 (2005).
[Crossref]

Bourgoin, M. O.

S. J. Smith, M. O. Bourgoin, K. Sims, and H. L. Voorhees, “Handwritten character classification using nearest neighbor in large databases,” IEEE Trans. Pattern Anal. Mach. Intell. 16, 915–919 (1994).
[Crossref]

Chu, D.

T. Sun, F. Xing, X. Wang, Z. You, and D. Chu, “An accuracy measurement method for star trackers based on direct astronomic observation,” Sci. Rep. 6, 22593 (2016).
[Crossref]

Enright, J.

I. Jovanovic and J. Enright, “Towards star tracker geolocation for planetary navigation,” in Proceedings of IEEE Aerospace Conference (2017), pp. 1–6

Fang, J.

J. Fang and X. Ning, “Installation direction analysis of star sensors by hybrid condition number,” IEEE Trans. Instrum. Meas. 58, 3576–3582 (2009).
[Crossref]

Fischer, J.

P. Jayaraman, J. Fischer, A. Moorhouse, and M. Lauer, “Star tracker operational usage in different phases of the Mars Express mission,” in Proceedings of SpaceOps 2006 Conference (2013).

Fu-Long, M. A.

B. O. Hua, M. A. Fu-Long, and L. C. Jiao, “Research on computation of GLCM of image texture,” Acta Electron. Sin. 1, 155–158 (2006).

Glenar, D. A.

D. A. Glenar, T. J. Stubbs, J. M. Hahn, and Y. Wang, “Search for a high-altitude lunar dust exosphere using Clementine navigational star tracker measurements,” J. Geophys. Res. Planets 119, 2548–2567 (2014).
[Crossref]

Hahn, J. M.

D. A. Glenar, T. J. Stubbs, J. M. Hahn, and Y. Wang, “Search for a high-altitude lunar dust exosphere using Clementine navigational star tracker measurements,” J. Geophys. Res. Planets 119, 2548–2567 (2014).
[Crossref]

Hua, B. O.

B. O. Hua, M. A. Fu-Long, and L. C. Jiao, “Research on computation of GLCM of image texture,” Acta Electron. Sin. 1, 155–158 (2006).

Jayaraman, P.

P. Jayaraman, J. Fischer, A. Moorhouse, and M. Lauer, “Star tracker operational usage in different phases of the Mars Express mission,” in Proceedings of SpaceOps 2006 Conference (2013).

Jiang, J.

Jiao, L. C.

B. O. Hua, M. A. Fu-Long, and L. C. Jiao, “Research on computation of GLCM of image texture,” Acta Electron. Sin. 1, 155–158 (2006).

Jovanovic, I.

I. Jovanovic and J. Enright, “Towards star tracker geolocation for planetary navigation,” in Proceedings of IEEE Aerospace Conference (2017), pp. 1–6

Lauer, M.

P. Jayaraman, J. Fischer, A. Moorhouse, and M. Lauer, “Star tracker operational usage in different phases of the Mars Express mission,” in Proceedings of SpaceOps 2006 Conference (2013).

Li, B.

T. Sun, F. Xing, Z. You, X. Wang, and B. Li, “Deep coupling of star tracker and MEMS-gyro data under highly dynamic and long exposure conditions,” Meas. Sci. Technol. 25, 085003 (2014).
[Crossref]

T. Sun, F. Xing, Z. You, X. Wang, and B. Li, “Smearing model and restoration of star image under conditions of variable angular velocity and long exposure time,” Opt. Express 22, 6009–6024 (2014).
[Crossref]

Li, J.

Liebe, C. C.

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

Moorhouse, A.

P. Jayaraman, J. Fischer, A. Moorhouse, and M. Lauer, “Star tracker operational usage in different phases of the Mars Express mission,” in Proceedings of SpaceOps 2006 Conference (2013).

Munur, M.

M. Berman and M. Munur, “Validating microsatellite star tracker baffle tests,” in Proceedings of AIAA/AAS Astrodynamics Specialist Conference (2013).

Ning, X.

J. Fang and X. Ning, “Installation direction analysis of star sensors by hybrid condition number,” IEEE Trans. Instrum. Meas. 58, 3576–3582 (2009).
[Crossref]

Pedrini, H.

F. R. D. Siqueira, W. R. Schwartz, and H. Pedrini, “Multi-scale gray level co-occurrence matrices for texture description,” Neurocomputing 120, 336–345 (2013).
[Crossref]

Potter, J. E.

J. E. Potter and W. W. E. Vande, “Optimum mixing of gyroscope and star tracker data,” J. Spacecr. Rockets 5, 536–540 (1968).
[Crossref]

Ristad, E. S.

E. S. Ristad and P. N. Yianilos, “Learning string-edit distance,” IEEE Trans. Pattern Anal. Mach. Intell. 20, 522–532 (1998).
[Crossref]

Saul, L. K.

K. Q. Weinberger and L. K. Saul, “Distance metric learning for large margin nearest neighbor classification,” J. Mach. Learn. Res. 10, 207–244 (2009).

Schwartz, W. R.

F. R. D. Siqueira, W. R. Schwartz, and H. Pedrini, “Multi-scale gray level co-occurrence matrices for texture description,” Neurocomputing 120, 336–345 (2013).
[Crossref]

Sims, K.

S. J. Smith, M. O. Bourgoin, K. Sims, and H. L. Voorhees, “Handwritten character classification using nearest neighbor in large databases,” IEEE Trans. Pattern Anal. Mach. Intell. 16, 915–919 (1994).
[Crossref]

Siqueira, F. R. D.

F. R. D. Siqueira, W. R. Schwartz, and H. Pedrini, “Multi-scale gray level co-occurrence matrices for texture description,” Neurocomputing 120, 336–345 (2013).
[Crossref]

Smith, S. J.

S. J. Smith, M. O. Bourgoin, K. Sims, and H. L. Voorhees, “Handwritten character classification using nearest neighbor in large databases,” IEEE Trans. Pattern Anal. Mach. Intell. 16, 915–919 (1994).
[Crossref]

Soh, L. K.

L. K. Soh and C. Tsatsoulis, “Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices,” IEEE Trans. Geosci. Remote Sens. 37, 780–795 (1999).
[Crossref]

Stubbs, T. J.

D. A. Glenar, T. J. Stubbs, J. M. Hahn, and Y. Wang, “Search for a high-altitude lunar dust exosphere using Clementine navigational star tracker measurements,” J. Geophys. Res. Planets 119, 2548–2567 (2014).
[Crossref]

Sun, T.

S. Zhang, F. Xing, T. Sun, Z. You, and M. Wei, “Novel approach to improve the attitude update rate of a star tracker,” Opt. Express 26, 5164–5181 (2018).
[Crossref]

G. Wang, F. Xing, M. Wei, T. Sun, and Z. You, “Optimization method for star tracker orientation in the sun-pointing mode,” Chin. Opt. Lett. 15, 081201 (2017).
[Crossref]

G. Wang, F. Xing, M. Wei, T. Sun, and Z. You, “Optimization method of star tracker orientation for sun-synchronous orbit based on space light distribution,” Appl. Opt. 56, 4480–4490 (2017).
[Crossref]

T. Sun, F. Xing, X. Wang, J. Li, M. Wei, and Z. You, “Effective star tracking method based on optical flow analysis for star trackers,” Appl. Opt. 55, 10335–10340 (2016).
[Crossref]

T. Sun, F. Xing, X. Wang, Z. You, and D. Chu, “An accuracy measurement method for star trackers based on direct astronomic observation,” Sci. Rep. 6, 22593 (2016).
[Crossref]

T. Sun, F. Xing, Z. You, X. Wang, and B. Li, “Deep coupling of star tracker and MEMS-gyro data under highly dynamic and long exposure conditions,” Meas. Sci. Technol. 25, 085003 (2014).
[Crossref]

T. Sun, F. Xing, Z. You, X. Wang, and B. Li, “Smearing model and restoration of star image under conditions of variable angular velocity and long exposure time,” Opt. Express 22, 6009–6024 (2014).
[Crossref]

T. Sun, F. Xing, Z. You, and M. Wei, “Motion-blurred star acquisition method of the star tracker under high dynamic conditions,” Opt. Express 21, 20096–20110 (2013).
[Crossref]

Tsatsoulis, C.

L. K. Soh and C. Tsatsoulis, “Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices,” IEEE Trans. Geosci. Remote Sens. 37, 780–795 (1999).
[Crossref]

Vande, W. W. E.

J. E. Potter and W. W. E. Vande, “Optimum mixing of gyroscope and star tracker data,” J. Spacecr. Rockets 5, 536–540 (1968).
[Crossref]

Voorhees, H. L.

S. J. Smith, M. O. Bourgoin, K. Sims, and H. L. Voorhees, “Handwritten character classification using nearest neighbor in large databases,” IEEE Trans. Pattern Anal. Mach. Intell. 16, 915–919 (1994).
[Crossref]

Wang, G.

Wang, X.

T. Sun, F. Xing, X. Wang, J. Li, M. Wei, and Z. You, “Effective star tracking method based on optical flow analysis for star trackers,” Appl. Opt. 55, 10335–10340 (2016).
[Crossref]

T. Sun, F. Xing, X. Wang, Z. You, and D. Chu, “An accuracy measurement method for star trackers based on direct astronomic observation,” Sci. Rep. 6, 22593 (2016).
[Crossref]

T. Sun, F. Xing, Z. You, X. Wang, and B. Li, “Deep coupling of star tracker and MEMS-gyro data under highly dynamic and long exposure conditions,” Meas. Sci. Technol. 25, 085003 (2014).
[Crossref]

T. Sun, F. Xing, Z. You, X. Wang, and B. Li, “Smearing model and restoration of star image under conditions of variable angular velocity and long exposure time,” Opt. Express 22, 6009–6024 (2014).
[Crossref]

Wang, Y.

D. A. Glenar, T. J. Stubbs, J. M. Hahn, and Y. Wang, “Search for a high-altitude lunar dust exosphere using Clementine navigational star tracker measurements,” J. Geophys. Res. Planets 119, 2548–2567 (2014).
[Crossref]

Wei, M.

Weinberger, K. Q.

K. Q. Weinberger and L. K. Saul, “Distance metric learning for large margin nearest neighbor classification,” J. Mach. Learn. Res. 10, 207–244 (2009).

Wolfenbarger, N. S.

N. S. Wolfenbarger, “Characterization of star tracker distortion for the ICESat mission,” Doctoral dissertation (The University of Texas, 2017).

Xing, F.

M. Wei, F. Xing, and Z. You, “A real-time detection and positioning method for small and weak targets using a 1D morphology-based approach in 2D images,” Light Sci. Appl. 7, 18006 (2018).
[Crossref]

S. Zhang, F. Xing, T. Sun, Z. You, and M. Wei, “Novel approach to improve the attitude update rate of a star tracker,” Opt. Express 26, 5164–5181 (2018).
[Crossref]

G. Wang, F. Xing, M. Wei, T. Sun, and Z. You, “Optimization method for star tracker orientation in the sun-pointing mode,” Chin. Opt. Lett. 15, 081201 (2017).
[Crossref]

G. Wang, F. Xing, M. Wei, T. Sun, and Z. You, “Optimization method of star tracker orientation for sun-synchronous orbit based on space light distribution,” Appl. Opt. 56, 4480–4490 (2017).
[Crossref]

T. Sun, F. Xing, X. Wang, J. Li, M. Wei, and Z. You, “Effective star tracking method based on optical flow analysis for star trackers,” Appl. Opt. 55, 10335–10340 (2016).
[Crossref]

T. Sun, F. Xing, X. Wang, Z. You, and D. Chu, “An accuracy measurement method for star trackers based on direct astronomic observation,” Sci. Rep. 6, 22593 (2016).
[Crossref]

T. Sun, F. Xing, Z. You, X. Wang, and B. Li, “Deep coupling of star tracker and MEMS-gyro data under highly dynamic and long exposure conditions,” Meas. Sci. Technol. 25, 085003 (2014).
[Crossref]

T. Sun, F. Xing, Z. You, X. Wang, and B. Li, “Smearing model and restoration of star image under conditions of variable angular velocity and long exposure time,” Opt. Express 22, 6009–6024 (2014).
[Crossref]

T. Sun, F. Xing, Z. You, and M. Wei, “Motion-blurred star acquisition method of the star tracker under high dynamic conditions,” Opt. Express 21, 20096–20110 (2013).
[Crossref]

Yan, J.

Yianilos, P. N.

E. S. Ristad and P. N. Yianilos, “Learning string-edit distance,” IEEE Trans. Pattern Anal. Mach. Intell. 20, 522–532 (1998).
[Crossref]

You, Z.

M. Wei, F. Xing, and Z. You, “A real-time detection and positioning method for small and weak targets using a 1D morphology-based approach in 2D images,” Light Sci. Appl. 7, 18006 (2018).
[Crossref]

S. Zhang, F. Xing, T. Sun, Z. You, and M. Wei, “Novel approach to improve the attitude update rate of a star tracker,” Opt. Express 26, 5164–5181 (2018).
[Crossref]

G. Wang, F. Xing, M. Wei, T. Sun, and Z. You, “Optimization method for star tracker orientation in the sun-pointing mode,” Chin. Opt. Lett. 15, 081201 (2017).
[Crossref]

G. Wang, F. Xing, M. Wei, T. Sun, and Z. You, “Optimization method of star tracker orientation for sun-synchronous orbit based on space light distribution,” Appl. Opt. 56, 4480–4490 (2017).
[Crossref]

T. Sun, F. Xing, X. Wang, J. Li, M. Wei, and Z. You, “Effective star tracking method based on optical flow analysis for star trackers,” Appl. Opt. 55, 10335–10340 (2016).
[Crossref]

T. Sun, F. Xing, X. Wang, Z. You, and D. Chu, “An accuracy measurement method for star trackers based on direct astronomic observation,” Sci. Rep. 6, 22593 (2016).
[Crossref]

T. Sun, F. Xing, Z. You, X. Wang, and B. Li, “Deep coupling of star tracker and MEMS-gyro data under highly dynamic and long exposure conditions,” Meas. Sci. Technol. 25, 085003 (2014).
[Crossref]

T. Sun, F. Xing, Z. You, X. Wang, and B. Li, “Smearing model and restoration of star image under conditions of variable angular velocity and long exposure time,” Opt. Express 22, 6009–6024 (2014).
[Crossref]

T. Sun, F. Xing, Z. You, and M. Wei, “Motion-blurred star acquisition method of the star tracker under high dynamic conditions,” Opt. Express 21, 20096–20110 (2013).
[Crossref]

Zhang, G.

Zhang, S.

Acta Electron. Sin. (1)

B. O. Hua, M. A. Fu-Long, and L. C. Jiao, “Research on computation of GLCM of image texture,” Acta Electron. Sin. 1, 155–158 (2006).

Appl. Opt. (2)

Chin. Opt. Lett. (1)

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

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

IEEE Trans. Geosci. Remote Sens. (1)

L. K. Soh and C. Tsatsoulis, “Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices,” IEEE Trans. Geosci. Remote Sens. 37, 780–795 (1999).
[Crossref]

IEEE Trans. Instrum. Meas. (1)

J. Fang and X. Ning, “Installation direction analysis of star sensors by hybrid condition number,” IEEE Trans. Instrum. Meas. 58, 3576–3582 (2009).
[Crossref]

IEEE Trans. Pattern Anal. Mach. Intell. (2)

S. J. Smith, M. O. Bourgoin, K. Sims, and H. L. Voorhees, “Handwritten character classification using nearest neighbor in large databases,” IEEE Trans. Pattern Anal. Mach. Intell. 16, 915–919 (1994).
[Crossref]

E. S. Ristad and P. N. Yianilos, “Learning string-edit distance,” IEEE Trans. Pattern Anal. Mach. Intell. 20, 522–532 (1998).
[Crossref]

J. Geophys. Res. Planets (1)

D. A. Glenar, T. J. Stubbs, J. M. Hahn, and Y. Wang, “Search for a high-altitude lunar dust exosphere using Clementine navigational star tracker measurements,” J. Geophys. Res. Planets 119, 2548–2567 (2014).
[Crossref]

J. Mach. Learn. Res. (1)

K. Q. Weinberger and L. K. Saul, “Distance metric learning for large margin nearest neighbor classification,” J. Mach. Learn. Res. 10, 207–244 (2009).

J. Spacecr. Rockets (1)

J. E. Potter and W. W. E. Vande, “Optimum mixing of gyroscope and star tracker data,” J. Spacecr. Rockets 5, 536–540 (1968).
[Crossref]

Light Sci. Appl. (1)

M. Wei, F. Xing, and Z. You, “A real-time detection and positioning method for small and weak targets using a 1D morphology-based approach in 2D images,” Light Sci. Appl. 7, 18006 (2018).
[Crossref]

Meas. Sci. Technol. (1)

T. Sun, F. Xing, Z. You, X. Wang, and B. Li, “Deep coupling of star tracker and MEMS-gyro data under highly dynamic and long exposure conditions,” Meas. Sci. Technol. 25, 085003 (2014).
[Crossref]

Neurocomputing (1)

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[Crossref]

Opt. Express (4)

Sci. Rep. (1)

T. Sun, F. Xing, X. Wang, Z. You, and D. Chu, “An accuracy measurement method for star trackers based on direct astronomic observation,” Sci. Rep. 6, 22593 (2016).
[Crossref]

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N. S. Wolfenbarger, “Characterization of star tracker distortion for the ICESat mission,” Doctoral dissertation (The University of Texas, 2017).

I. Jovanovic and J. Enright, “Towards star tracker geolocation for planetary navigation,” in Proceedings of IEEE Aerospace Conference (2017), pp. 1–6

P. Jayaraman, J. Fischer, A. Moorhouse, and M. Lauer, “Star tracker operational usage in different phases of the Mars Express mission,” in Proceedings of SpaceOps 2006 Conference (2013).

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Supplementary Material (1)

NameDescription
» Visualization 1       Continuous star tracking results under different light conditions.

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Figures (13)

Fig. 1.
Fig. 1. Flow chart of the proposed method.
Fig. 2.
Fig. 2. Typical energy distribution of a star spot in the real night sky observation experiment.
Fig. 3.
Fig. 3. Several types of interference speckles in real night sky observation experiment: (a) is noise in single pixel; (b) is noise in a small region; (c) is noise distributed in a number of pixels.
Fig. 4.
Fig. 4. Star images affected (a) by sunlight; (b) by cloud-like stray light; (c) by moonlight.
Fig. 5.
Fig. 5. Background results of different star images (a) with sunlight, (b) with cloud-like stray light, and (c) with moonlight.
Fig. 6.
Fig. 6. Distribution of the extracted spots and the differential encoding operation: (a) is an ideal star spot, (b) is a general star spot, and (c) is noise; (d), (e) and (f) are three-dimensional displays of energy distribution for (a), (b) and (c), separately.
Fig. 7.
Fig. 7. (a) Real night sky observation experiments and (b) the star tracker in the experiments.
Fig. 8.
Fig. 8. (a) L-Distance results of the star spot and the noise (the black color represents the recognized star spots, and the red color represents the interference spots) and (b) the occurrence frequency of different L-Distance values.
Fig. 9.
Fig. 9. Results of various types of star spots with different star magnitudes and stray light conditions.
Fig. 10.
Fig. 10. L-Distance results of several types of noise spots.
Fig. 11.
Fig. 11. Star extraction and identification results for type I star images (with sunlight); (a) is the extraction result with the frequently used threshold segmentation algorithm, and (b)–(d) are extraction results with our method along with the changes of the background (see Visualization 1).
Fig. 12.
Fig. 12. Star extraction and identification results for type II star images (with cloud-like stray light); (a) is the extraction result with the frequently used threshold segmentation algorithm and (b)–(d) are extraction results with our method along with the changes of the background.
Fig. 13.
Fig. 13. Star extraction and identification results for type III star images (with moonlight); (a) is the extraction result with the frequently used threshold segmentation algorithm, and (b)–(d) are extraction results with our method along with the changes of the background.

Tables (5)

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Table 1. GLCM Values for a General Star Image

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Table 2. GLCM Values for a Star Image with Sunlight

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Table 3. GLCM Values for a Star Image with Cloud-Like Stray Light

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Table 4. GLCM Values for a Star Image with Moonlight

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Table 5. Results of L-Distance

Equations (6)

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C Δ x , Δ y ( i , j ) = x = 1 n y = 1 m { 1 , if    I ( x , y ) = i  and I ( x + Δ x , y + Δ y ) = j 0 , otherwise .
c ( y i ) = arg max y j D k I ( k ( y j ) , c p ) , p = 1 , 2 , 3 , N ,
b ( i , j ) = { 1 , i , j D b 0 , otherwise .
t = f b = ( f Θ b ) b , L b = { L Label 1 L Label 2 L Label 3 .
D = { 1 g i , j g i 1 , j 0 , 0 g i , j g i 1 , j < 0 , i , j W 5 × 5 .
lev s 1 , s 2 ( i , j ) = { max ( i , j ) if    min ( i , j ) = 0 , min { lev s 1 , s 2 ( i 1 , j ) + 1 lev s 1 , s 2 ( i , j 1 ) + 1 lev s 1 , s 2 ( i 1 , j 1 ) + 1 ( s 1 ( i ) s 2 ( j ) ) otherwise .

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