CUDA-Based Method to Boost Target Performance Evaluation of Space Systems for Automatic Mobile Object Identification and Localization

Abstract

The complex organization and application conditions of space systems for automatic identification and localization of mobile objects, which include the automatic identification system (AIS) and automatic dependent surveillance-broadcast (ADS-B) system, determine the choice of simulation models for the mathematical formalization of their operation. Simulation modeling of satellite constellations capable of receiving, processing, and retransmitting AIS and ADS-B signals can take a significant amount of time when being used to substantiate circuit design solutions for satellites and plans for their further application given a large number of radiation sources to be simulated (e.g., for the AIS, their number exceeds 500 thousand). One of the methods for solving this problem is parallel computing based on the compute unified device architecture (CUDA) technology. However, due to the specificity of machine instruction execution on NVIDIA GPUs, software quality depends heavily on GPU memory allocation efficiency and algorithms for program code execution. In this paper, we propose a method for target performance evaluation of space systems for automatic identification and localization of mobile objects; the method uses massively parallel computations on GPUs to provide a significant reduction in simulation time, which is especially important for multi-satellite constellations. The efficiency of the method is confirmed by model-cybernetic experiments carried out on various software and hardware platforms.

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REFERENCES

  1. 1

    Andreev, A.M., Analyzing the development of a satellite automatic identification system for ship traffic monitoring. Part 2: Monitoring tools, Tr. Voen.-Kosm. Akad. im. A.F. Mozhaiskogo (Proc. Mozhaysky Mil. Space Acad.), 2016, no. 652, pp. 7–15.

  2. 2

    Romanov, A.A., Romanov, A.A., and Tyulin, A.E., Small-sized mobile object monitoring satellites of JSC Russian Space Systems: State and prospects, Raketno-Kosm.Priborostr. Inf. Sist., 2015, vol. 2, no. 1, pp. 3–10.

    Google Scholar 

  3. 3

    Hoye, G., Eriksen, T., Meland, B.J., and Narheim, G., Space-based AIS for global maritime traffic monitoring, Acta Astronautica, 2008, vol. 62, pp. 240–245.

    Article  Google Scholar 

  4. 4

    Trong, T.V., Dinh, Q.T., Van, T.D., Quang, H.P., and Nguyen, H., Constellation of small quick-launched and self-deorbiting nano-satellites with AIS receivers for global ship traffic monitoring, Proc. 2nd Nano-Satellite Symp., Tokyo, 2011. https://uu.divaportal.org/smash/get/diva2:424564/ FULLTEXT01.pdf. Accessed September 5, 2016.

  5. 5

    Van Der Pryt, R. and Vincent, R., The CanX-7 nanosatellite ADS-B mission: A preliminary assessment, Positioning, 2017, vol. 8, pp. 1–11.

    Article  Google Scholar 

  6. 6

    Brodsky, Y., Rieber, R., and Nordheim, T., Balloon-borne air traffic management (ATM) as a precursor to space-based ATM, Acta Astronautica, 2012, vol. 70, pp. 112–121.

    Article  Google Scholar 

  7. 7

    Barson, J.V., Automatic dependent surveillance-broadcast: The first step in the FAA’s next-generation air transportation system, Aviat., Space Environ. Med., 2009, vol. 80, no. 4, pp. 422–423.

    Article  Google Scholar 

  8. 8

    Werner, K., Bredemeyer, J., and Delovski, T., ADS-B over satellite: Global air traffic surveillance from space, Proc. Tyrrhenian Int. Workshop Digital Communications, Enhanced Surveillance of Aircraft and Vehicles (TIWDC/ESAV), 2014, pp. 47–52.

  9. 9

    N2YO. https://www.n2yo.com.

  10. 10

    Marine traffic. https://www.marinetraffic.com/ru.

  11. 11

    VesselFinder. https://www.vesselfinder.com.

  12. 12

    MyShipTracking. https://www.myshiptracking.com.

  13. 13

    Flightradar24. https://www.flightradar24.com.

  14. 14

    ADS-B exchange. https://www.adsbexchange.com.

  15. 15

    Planefinder. https://planefinder.net.

  16. 16

    Skorokhodov, Ya.A., Simulating the operation of space-based and ground-based air traffic monitoring systems, Tr. S.-Peterb. Inst. Inf. Avtom. Ross. Akad. Nauk (Proc. St. Petersburg Inst. Inf. Autom. Russ. Acad. Sci.), 2018, vol. 6, no. 61, pp. 29–60. https://doi.org/10.15622/sp.61.2

    Article  Google Scholar 

  17. 17

    Skorokhodov, Ya.A. and Andreev, A.M., Simulating the operation of the space segment of the automatic ship identification system, Inf.-Upr. Sist., 2018, vol. 2, no. 93, pp. 36–48. https://doi.org/10.15217/issn1684-8853.2018.2.36

    Article  Google Scholar 

  18. 18

    Skorokhodov, Ya.A., Makhrov, K.B., and Malyshev, D.V., Simulation model of the operation of the space system for ship traffic monitoring, Tr. Voen.-Kosm. Akad. im. A.F. Mozhaiskogo (Proc. Mozhaysky Mil. Space Acad.), 2017, no. 657, pp. 23–33.

  19. 19

    Kuznetsov, A.M. and Romanov, A.A., Simulating the reception of AIS signal collisions on the board of the spacecraft, Raketno-Kosm.Priborostr. Inf. Sist., 2015, vol. 2, no. 1, pp. 25–36.

    Google Scholar 

  20. 20

    Mendes, S., Amado, S., Teresa, V., Scorzolini, A., Perini, V., and Sorbo, A., Satellite AIS: An end-to-end simulation approach, Proc. 11th Int. WS Simulation and EGSE Facilities for Space Programmes, 2010, Noordwijk, Netherlands. https://indico.esa.int/indico/event/109/session/15/ contribution/48/ material/0/0.pdf.

  21. 21

    Chen, Y., Research on detection probability of space-based AIS for real scenarios. http://ijssst.info/Vol-17/No-30/paper3.pdf. https://doi.org/10.5013/IJSSST.a.17.30.03

  22. 22

    Menghui, Y., Yongzhong, Z., and Li, F., Collision and detection performance with three overlap signal collisions: Space-based AIS reception, Proc. 11th Int. Conf. Trust, Security, and Privacy in Computing and Communications, Liverpool, 2012, pp. 1641–1648.

  23. 23

    Van Der Pryt, R. and Vincent, R., A simulation of signal collisions over the North Atlantic for a spaceborne ADS-B receiver using Aloha protocol, Positioning, 2015, vol. 6, no. 3, pp. 23–31.

    Article  Google Scholar 

  24. 24

    Van Der Pryt, R. and Vincent, R., A simulation of the reception of automatic dependent surveillance-broadcast signals in low earth orbit, Int. J. Navig. Obs., 2015.

  25. 25

    Technical characteristics for an automatic identification system using time-division multiple access in the VHF maritime mobile band. https://www.itu.int/rec/R-REC-M.1371-3-200706-S/en.

  26. 26

    Dynamic statistical studies on satellite reception of the ADS-B signal for global flight tracking for civil aviation, Proc. World Radiocommunication Conf. (WRC), Geneva, 2015. https://www.itu.int/md/R15-WRC15-C-0100/en.

  27. 27

    REPORT ITU-R M.2084, Satellite detection of automatic identification system messages. https://www.itu.int/pub/R-REP-M.2084.

  28. 28

    NVIDIA, Developer zone. https://developer.nvidia.com.

  29. 29

    Boreskov, A.V. and Kharlamov, A.A., Osnovy raboty s tekhnologiei CUDA (Basics of Working with CUDA Technology), Moscow: DMK Press, 2010.

  30. 30

    Sanders, J. and Kandrot, E., CUDA by Example: An Introduction to General-Purpose GPU Programming, Boston: Addison-Wesley, 2010.

    Google Scholar 

  31. 31

    Roy, A.E., Orbital Motion, Bristol: Institute of Physics Publishing, 2005.

    Google Scholar 

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Correspondence to Ya. A. Skorokhodov.

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Translated by Yu. Kornienko

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Skorokhodov, Y.A. CUDA-Based Method to Boost Target Performance Evaluation of Space Systems for Automatic Mobile Object Identification and Localization. Program Comput Soft 45, 333–345 (2019). https://doi.org/10.1134/S0361768819060070

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