(44-3) 08 * << * >> * Russian * English * Content * All Issues

Structurally topological algorithm for star recognition and near-Earth space’ object detection

I.G. Zhurkin 1, L.N. Chaban  1, P.Yu. Orlov 1

1 Moscow State University of Geodesy and Cartography (MIIGAiK),

105064, Moscow, Russia, Gorokhovskii pereulok 4

 PDF, 878 kB

DOI: 10.18287/2412-6179-CO-597

Pages: 375-384.

Full text of article: Russian language.

Abstract:
When solving a variety of celestial navigation tasks there is a problem of determining parameters of spacecraft motion and onboard primary payload orientation based on the coordinates of registered star images. Furthermore, unwanted objects, like active satellites, natural and artificial space debris, that reduce the probability of correct recognition may get into the field of view of a satellite sensor. This prompts the necessity to filter out such interference from the star field images. However, if the objects under recognition are bodies located in near-Earth space, in this case, the star images themselves will act as interferences. In addition, since the detection and cataloging of these objects from the Earth’s surface is complicated by their small size, the atmospheric effects, as well as other technical difficulties, it is worthwhile to use the existing equipment onboard spacecrafts to solve this task. The existing recognition algorithms for star groups, as well as their classification, are presented in this paper. Moreover, a structurally topological approach for identifying groups of stars based on the properties of enveloping polygons used in constructing topological star patterns is proposed. Specific features in the construction of topological configurations on the analyzed set of points, as well as the principles of dynamic space object detection within their limits are described. Results of the numerical experiments performed using the developed algorithm on the star field maps and model scenes are presented.

Keywords:
star recognition, star pattern, digital image processing, autonomous navigation, remote sensing, near-Earth space, space debris, space object.

Citation:
Zhurkin IG, Chaban LN, Orlov PYu. Structurally topological algorithm for star recognition and near-Earth space’ object detection. Computer Optics 2020; 44(3): 375-384. DOI: 10.18287/2412-6179-CO-597.

Acknowledgements:
The research was carried out within the state assignment of the Ministry of Science and Higher Education of the Russian Federation (No. 5.6680.2017/8.9).

References:
  1. Altukhov AI, Korshunov DS, Shabakov EI. Requirements for image quality of emergency spacecrafts [In Russian]. Scientific and Technical Journal of Information Technologies, Mechanics and Optics 2015; 15(3): 405-410. DOI: 10.17586/2226-1494-2015-15-3-405-410.
  2. Baklanov AI, Blinov VD, Gorbunov IA, Zabiyakin AS, Malakhov IA. High resolution equipment for the future spacecraft “Resource-PM” [In Russian]. Vestnik of the Samara State Aerospace University 2016; 15(2): 30-35. DOI: 10.18287/2412-7329-2016-15-2-30-35.
  3. Blinov VN, Sechenov YuN, Shalai VV. Small satellites [In Russian]. Omsk: OmSTU Publisher; 2016.
  4. Gaivoronskii SV, Rusin EV, Tsodokova VV. Stars identification at the astronomical coordinates determination by means of an automated zenith telescope [In Russian]. Scientific and Technical Journal of Information Technologies, Mechanics and Optics 2015; 15(1): 22-29. DOI: 10.17586/2226-1494-2015-15-1-22-29.
  5. Grenander U. Lectures in pattern theory. Vol. 1: Pattern synthesis. New York: Springer-Verlag; 1976. ISBN: 978-0-387-90174-9.
  6. Gruzdev SS, Zhurkin IG, Orlov PYu, Pankin AV. Analysis of the current state and prospects for the development of a digital catalog of objects in near-Earth space [In Russian]. Sovremennye Problemy Distantsionnogo Zondirovaniya Zemli iz Kosmosa 2016; 13(6): 51-59. DOI: 10.21046/2070-7401-2016-13-6-51-59.
  7. Zhurkin IG, Tolkachev PB. An algorithm for identifying stars on a pair of overlapping star field's images [In Russian]. Izvestiya Vysshikh Uchebnykh Zavedenii: Geodeziya i Aerofotosjemka 1991; 2: 59-65.
  8. Kruzhilov IS. Methods and software tools to improve the efficiency of stars’ groups recognition in autonomous star tracking [In Russian]. The thesis for the Candidate’s degree in Technical Sciences. Moscow: MPEI; 2010.
  9. Kuznetsov VI, Danilova TV. Identification algorithms of “operational” stars on a sidereal field [In Russian]. Izvestiya vysshikh uchebnykh zavedeniy. Priborostroenie 2003; 46(4): 17-23.
  10. Leukhin AN. Multidimensional hypercomplex contour analysis and its applications to image and signal processing [In Russian]. Dr phys-math sci thesis. Yoshkar-Ola: Mari State Technical University Publisher; 2004.
  11. Fu KS. Syntactic methods in pattern recognition. New York: Academic Press; 1974.
  12. Furman YaA, Krevetskii AV, Peredreev AK, Rozhentsov AA, Khafizov RG, Egoshina IL, Leukhin AN. Introduction to contour analysis; applications for image and signal processing [In Russian]. Moscow: “Fizmatlit”; 2003.
  13. Furman YaA, Krevetskii AV, Rozhentsov AA, Khafizov RG, Leukhin AN, Egoshina IL. Complex-valued and hypercomplex systems in the tasks of multidimensional signals processing [In Russian]. Moscow: “Fizmatlit” Publisher; 2004.
  14. Alvelda P, San Martin AM. Neural network star pattern recognition for spacecraft attitude determination and control. Advances in Neural Information Processing Systems1: Proceedings of the 1988 conference 1989; 314-322.
  15. Duan Y, Li M, Niu Z, Jing P, Chen Z. A star pattern recognition algorithm for cameras with large FOV. Journal of Modern Optics 2018; 65(1): 85-97. DOI: 10.1080/09500340.2017.1377304.
  16. Hong J, Dickerson JA. Neural-network-based autonomous star identification algorithm. Journal of Guidance, Control, and Dynamics 2000; 23: 728-735. DOI: 10.2514/2.4589.
  17. Kolomenkin M, Pollak S, Shimshoni I, Lindenbaum M. Geometric voting algorithm for star trackers. IEEE Transactions on Aerospace and Electronic Systems 2008; 44: 441-456. DOI: 10.1109/TAES.2008.4560198.
  18. Mortari D. A fast on-board autonomous attitude determination system based on a new star-id technique for a wide FOV star tracker. Advances in the Astronautical Sciences 1996; 93: 893-903.
  19. Mortari D. Search-less algorithm for star pattern recognition. Advances in the Astronautical Sciences 1997; 45: 179-194.
  20. Mortari D. K-vector range searching techniques. Advances in the Astronautical Sciences 2000; 105: 449-464.
  21. Padgett C, Delgado KK. A grid algorithm for autonomous star identification. IEEE Transactions on Aerospace and Electronic Systems 1997; 33: 202-213. DOI: 10.1109/7.570743
  22. Spratling BB IV, Mortari D. A Survey on Star Identification Algorithms. Algorithms 2009; 2(1): 93-107. DOI: 10.3390/a2010093.
  23. Azmerit. Compact Star Trackers. Source: <http://www.azmerit.ru/project.html>.
  24. Optico-physical department. Family of BOKZ star trackers. Source: <http://ofo.ikiweb.ru/en/bokz.php>.

 

© 2009, IPSI RAS
151, Molodogvardeiskaya str., Samara, 443001, Russia; E-mail: ko@smr.ru ; Tel: +7 (846) 242-41-24 (Executive secretary), +7 (846) 332-56-22 (Issuing editor), Fax: +7 (846) 332-56-20