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The Resident Space Object Detection Method Based on the Connection between the Fourier Domain Image of the Video Data Difference Frame and the Orbital Velocity Projection

  • Vasilina Baranova (Department of Physics and Aerospace Technology, Belarusian State University) ;
  • Alexander Spiridonov (Department of Physics and Aerospace Technology, Belarusian State University) ;
  • Dmitrii Ushakov (Department of Physics and Aerospace Technology, Belarusian State University) ;
  • Vladimir Saetchnikov (Department of Physics and Aerospace Technology, Belarusian State University)
  • Received : 2024.05.07
  • Accepted : 2024.08.30
  • Published : 2024.09.15

Abstract

A method for resident space object detection in video stream processing using a set of matched filters has been proposed. Matched filters are constructed based on the connection between the Fourier spectrum shape of the difference frame and the magnitude of the linear velocity projection onto the observation plane. Experimental data were obtained using the mobile optical surveillance system for low-orbit space objects. The detection problem in testing mode was solved for raw video data with intensity signals from three satellites: KORONAS-FOTON, CUSAT 2/FALCON 9, and GENESIS-1. Difference frames of video data with the AQUA satellite pass were used to construct matched filters. The satellites were automatically detected at points where the difference in the value of their linear velocity projection and the reference satellite was close in value. An initial approximation of the satellites slant range vector and position vector has been obtained based on the values of linear velocity projection onto the frame plane. It has been established that the difference in the inclination angle between the detected satellite intensity signal Fourier image and the reference satellite mask corresponds to the difference in the inclinations of these objects. The proposed method allows for detecting and estimating the initial approximation of the slant range and position vector of artificial and natural space objects, such as satellites, debris, and asteroids.

Keywords

Acknowledgement

This work was supported by the Republic of Belarus' scientific research state programs, "High-tech Technologies and Equipment" and "Digital and space technologies, human, society and state security."

References

  1. Alton KB, Stepien K, CCD photometry, light curve deconvolution, period analysis and evolutionary status of the HADS variable v524, Acta Astron. 69, 283-304 (2019). https://doi.org/10.32023/0001-5237/69.3.4 
  2. Azimov AM, Tillayev YA, Ehgamberdiev SA, Ilyasov SP, Astronomical seeing at maidanak observatory with differential image motion monitor, J. Astron. Telesc. Instrum. Syst. 8, 047002 (2022). https://doi.org/10.1117/1.JATIS.8.4.047002 
  3. Baranova V, Spiridonov A, Liashkevich S, Saetchnikov V, Video data processing system for ground-based space optical surveillance application, Proceedings of the 2023 IEEE 10th International Workshop on Metrology for AeroSpace, Milan, Italy, 19-21 Jun 2023. 
  4. Baranova V, Spiridonov A, Ushakov D, Kozlov V, Cherny V, et al., Geometric approach to determining the space object orbit altitude using angels-only measurements, Proceedings of the 2024 11th International Workshop on Metrology for AeroSpace, Lublin, Poland, 3-5 Jun 2024. 
  5. Baranova VS, Saechnikov VA, Spiridonov AA, Autonomous streaming space objects detection based on a remote optical system, Dev. Methods Meas. 12, 272-279 (2021). https://doi.org/10.21122/2220-9506-2021-12-4-272-279 
  6. Blake J, Looking out for a sustainable space, Astron. Geophys. 63, 2.14-2.20 (2022). https://doi.org/10.1093/astrogeo/atac022 
  7. Blake JA, Chote P, Pollacco D, Feline W, Privett G, et al., DebrisWatch I: a survey of faint geosynchronous debris, Adv. Space Res. 67, 360-370 (2021). https://doi.org/10.1016/j.asr.2020.08.008 
  8. Boley AC, Byers M, Satellite mega-constellations create risks in low earth orbit, the atmosphere and on Earth, Sci. Rep. 11, 10642 (2021). https://doi.org/10.1038/s41598-021-89909-7 
  9. Chote P, Blake JA, Pollacco D, Precision optical light curves of LEO and GEO objects, in Advanced Maui Optical and Space Surveillance Technologies Conference (AMOS), Maui, HI, 1-4 Sep 2019. 
  10. Chun F, Tippets R, Della-Rose DJ, Daniel P, Kimberlee G, et al., The air force academy's Falcon telescope network: an educational and research network for K-12 and higher education, American Astronomical Society, in AAS Meeting #225, Seattle, WA, 4-8 Jan 2015. 
  11. Cooke BF, Chote P, Pollacco D, West R, Blake JA, et al., Simulated recovery of LEO objects using sCMOS blind stacking, Adv. Space Res. 72, 907-921 (2023). https://doi.org/10.1016/j.asr.2023.05.003 
  12. Danescu RG, Itu R, Muresan MP, Rednic A, Turcu V, SST anywhere: a portable solution for wide field low Earth orbit surveillance, Remote Sens. 14, 1905 (2022). https://doi.org/10.3390/rs14081905 
  13. Diprima F, Santoni F, Piergentili F, Fortunato V, Abbattista C, et al., Efficient and automatic image reduction framework for space debris detection based on GPU technology, Acta Astronaut. 145, 332-341 (2018). https://doi.org/10.1016/j.actaastro.2018.02.009 
  14. Dokkum P, Pasha I, A robust and simple method for filling in masked data in astronomical images, Publ. Astron. Soc. Pacific. 136, 034503 (2024). https://doi.org/10.1088/1538-3873/ad2866 
  15. Gonzalez R, Woods R, Digital Image Processing, 3rd ed. (Prentice-Hall, New York, 2006). 
  16. Guo X, Gao P, Shen M, Yang D, Yu H, et al., Introduction to APOSOS project: 15 cm aperture electro-optical telescopes to track space objects, Adv. Space Res. 65, 1990-2002 (2020). https://doi.org/10.1016/j.asr.2020.01.024 
  17. Hickson P, A fast algorithm for the detection of faint orbital debris tracks in optical images, Adv. Space Res. 62, 3078-3085 (2018). https://doi.org/10.1016/j.asr.2018.08.039 
  18. Hwang H, Park SY, Lee E, Angles-only initial orbit determination of low Earth orbit (LEO) satellites using real observational data, J. Astron. Space Sci. 36, 187-197 (2019). https://doi.org/10.5140/JASS.2019.36.3.187 
  19. Krantz H, Pearce EC, Block A, The steward observatory LEO satellite photometric survey, Publ. Astron. Soc. Pacific. 135, 095003 (2023). https://doi.org/10.1088/1538-3873/acf40c 
  20. Kyono T, Lucas J, Werth M, Calef B, McQuaid I, et al., Machine learning for quality assessment of ground-based optical images of satellites, Opt. Eng. 59, 051403 (2020). https://doi.org/10.1117/1.OE.59.5.051403 
  21. Lei X, Li Z, Du J, Chen J, Sang J, et al., Identification of uncatalogued LEO space objects by a ground-based EO array, Adv. Space Res. 67, 350-359 (2021). https://doi.org/10.1016/j.asr.2020.07.030 
  22. Lei X, Wang K, Zhang P, Pan T, Li H, et al., A geometrical approach to association of space-based very short-arc LEO tracks, Adv. Space Res. 62, 542-553 (2018). https://doi.org/10.1016/j.asr.2018.04.044 
  23. Levesque M, Automatic reacquisition of satellite positions by detecting their expected streaks in astronomical images, Proceedings of the Advanced Maui Optical and Space Surveillance Technologies Conference, Hawaii, HI, 1-4 Sep 2009. 
  24. Oniga F, Miron M, Danescu R, Nedevschi S, Automatic recognition of low Earth orbit objects from image sequences, in 2011 IEEE 7th International Conference on Intelligent Computer Communication and Processing, Cluj-Napoca, Romania, 25-27 Aug 2011. 
  25. Park JH, Yim HS, Choi YJ, Jo JH, Moon HK, et al., OWL-NET: a global network of robotic telescopes for satellite observation, Adv. Space Res. 62, 152-163 (2018). https://doi.org/10.1016/j.asr.2018.04.008 
  26. Pastor A, Sanjurjo-Rivo M, Escobar D, Initial orbit determination methods for track-to-track association, Adv. Space Res. 68, 2677-2694 (2021). https://doi.org/10.1016/j.asr.2021.06.042 
  27. Shakun L, Koshkin N, Korobeynikova E, Kozhukhov D, Kozhukhov O, et al., Comparative analysis of global optical observability of satellites in LEO, Adv. Space Res. 67, 1743-1760 (2021). https://doi.org/10.1016/j.asr.2020.12.021 
  28. Spiridonov A, Baranova V, Ushakov D, Saetchnikov V, Kenko Z, et al., University mobile optical surveillance system for lowEarth space object orbit determination, Proceedings of the 2022 IEEE 9th International Workshop on Metrology for AeroSpace (MetroAeroSpace), Pisa, Italy, 27-29 Jun 2022. 
  29. Spiridonov AA, Saetchnikov VA, Ushakov DV, Cherny VE, Kezik AG, Small satellite orbit determination methods based on the Doppler measurements by Belarusian State University ground station, IEEE J. Miniaturization Air Space Syst. 2, 59-66 (2021). https://doi.org/10.1109/JMASS.2020.3047456 
  30. Stark H, Application of Optical Fourier Transforms (Academic Press, Orlando, 1982). 
  31. Sun R, Zhan J, Zhao C, Zhang X, Algorithms and applications for detecting faint space debris in GEO, Acta Astronaut. 110, 9-17 (2015). https://doi.org/10.1016/j.actaastro.2015.01.001 
  32. Suthakar V, Sanvido AA, Qashoa R, Lee RSK, Comparative analysis of resident space object (RSO) detection methods, Sensors. 23, 9668 (2023). https://doi.org/10.3390/s23249668 
  33. Torteeka P, Gao P, Shen M, Guo X, Yang D, et al., Autonomous space target tracking through state estimation techniques via ground-based passive optical telescope, Adv. Space Res. 63, 461-475 (2019). https://doi.org/10.1016/j.asr.2018.09.012 
  34. Vallado DA, McClain W, Fundamentals of Astrodynamics and Applications (Microcosm Press, Torrance, 2013). 
  35. Wijnen TPG, Stuik R, Rodenhuis M, Langbroek M, Wijnja P, Using all-sky optical observations for automated orbit determination and prediction for satellites in low earth orbit, Proceedings of the 1st NEO and Debris Detection Conference, Darmstadt, Germany, 22-24 Jan 2019. 
  36. Wlodarczyk I, Cernis K, Boyle RP, Discovery, orbit and orbital evolution of the distant object (463368) 2012 vu85, Acta Astron. 67, 81 (2017). https://doi.org/10.32023/0001-5237/67.1.6 
  37. Yanagisawa T, Kurosaki H, Oda H, Tagawa M, Ground-based optical observation system for LEO objects, Adv. Space Res. 56, 414-420 (2015). https://doi.org/10.1016/j.asr.2015.01.019