The method of optimal planning of computing resources of the onboard computing complex of the spacecraft in the conditions of a difficult-to-predict increase in computing load


Аuthors

Popov D. G.*, Nesterenko O. E., Romanov A. V., Trepkov R. E.

Mlitary spaсe Aсademy named after A.F. Mozhaisky, Saint Petersburg, Russia

*e-mail: vka@mil.ru

Abstract

The work is devoted to the study of the issues of the effectiveness of the functioning of the on-board computer complex of the Earth remote sensing spacecraft in the process of data collection and processing, depending on the phono-target situation.

The issues of choosing the optimal planning of computing resources on the means of the onboard computing complex of the Earth remote sensing spacecraft in conditions of a difficult-to-predict increase in computing load are considered.

In cases where it is necessary to process information about a large number of observed objects, some of them may not be identified. First of all, this is due to the imperfection of existing information processing algorithms. In some cases, the information frames received for processing have very poor image quality as a result of the influence of various disturbing factors. In this regard, it becomes necessary to use all available computing resources to process information about difficult-to-identify observable objects. With large volumes of input information, as well as when solving a variety of tasks by an on-board computing complex, computing resources can be distributed unevenly. Some of the useful information may be lost, which will lead to a decrease in the reliability of the identification of surveillance objects.

The paper describes a step-by-step method for finding optimal planning of computing resources.

Due to the optimal planning of computing resources, or the redistribution of solved tasks or subtasks between all available computing modules, it is necessary to establish the dependence of the probability of reliable detection of observed objects on the quality and intensity of incoming information for processing.

In the work, the problem is formulated and the method of searching for optimal planning of computing resources is gradually described.

To solve the problem of planning computing resources, a simulation experiment was carried out, which is implemented in the software and algorithmic complex of the functioning of the onboard computer complex of the Earth remote sensing spacecraft in the object-oriented programming language C++.

When planning computing resources, the proposed approach takes into account the dependence of processing time on the number of observed objects received in information frames with low image quality, which can reduce the loss of useful information and thereby increase the reliability of the identification of observed objects.

After choosing the optimal plan of computing resources, the loss of useful information decreased, as a result of which the percentage of identifiable objects of observation increased.

References

  1. Popov D.G., Antonov D.A., Orkin V.V. Izvestiya Tul'skogo gosudarstvennogo universiteta, 2023, no. 4, pp. 80-86.

  2. Maletin A.N., Glushchenko A.A., Mishina O.A. Trudy MAI, 2022, no. 127. URL: https://trudymai.ru/eng/published.php?ID=170351. DOI: 10.34759/trd-2022-127-21

  3. Razin'kov S.N., Zhidko E.A. Informatsionno-izmeritel'nye i upravlyayushchie sistemy, 2018, vol. 16, no. 8, pp. 64–68.

  4. Razin'kov S.N., Sirota A.A. Izmeritel'naya tekhnika, 2004, no. 2, pp. 53–59.

  5. Mel'nikov Yu.P., Popov S.V. Radiotekhnicheskaya razvedka. Metody otsenki effektivnosti mestoopredeleniya istochnikov radioizlucheniya. (Radio engineering intelligence. Methods for assessing the effectiveness of locating radio sources), Moscow, Radiotekhnika, 2008, 432 p.

  6. Gurov G.B., Pozdyshev V.Yu., Timoshenko A.V., Razin'kova O.E. Vychislitel'nye tekhnologii, 2021, no. 26 (4), pp. 16–26. DOI: 10.25743/ICT.2021.26.4.003

  7. Zozulya Yu.T., Saranchenkov V.A., Stolyarov O.I. Vestnik Kontserna VKO “Almaz – Antei”, 2022, no. 4, pp. 80–90. DOI: 10.38013/2542-0542-2022-4-80-90

  8. Altiok T., Melamed B. Simulation modeling and analysis with Arena, Burlington, Elsevier Inc., 2007, 440 p.

  9. Vasil'ev V.I., Il'yasov B.G. Intellektual'nye sistemy upravleniya. Teoriya i praktika (Intelligent control systems. Theory and practice), Moscow, Radiotekhnika, 2009, 392 p.

  10. Koffman E.G. Teoriya raspisanii i vychislitel'nye mashiny (Theory of schedules and computing machines), Moscow, Nauka, 1984, 334 p.

  11. Guz D.S., Krasovskii D.V., Furugyan M.G. Effektivnye algoritmy planirovaniya vychislenii v mnogoprotsessornykh sistemakh real'nogo vremeni (Effective algorithms for planning computations in real-time multiprocessor systems), Moscow, VTs RAN, 2004, 66 p.

  12. Golosov P.E., Kozlov M.V., Malashenko Yu.E., Nazarova I.A., Ronzhin A.F. Model' sistemy upravleniya spetsializirovannym vychislitel'nym kompleksom: Soobshcheniya po prikladnoi matematike (Model of a control system for specialized computing compleksom: Messages on applied mathematics), Moscow, VTs RAN, 2010, 43 p.

  13. Yashkov S.F. Itogi nauki i tekhniki. Seriya: Teoriya veroyatnostei. Matematicheskaya statistika. Teoreticheskaya kibernetika, 1990, no. 29, pp. 3–82.

  14. Borst S., Boxma O., Groote J. F., Mauw S. Task allocation in a multi-server system // Journal of Scheduling, 2003, vol. 6, pp. 423–436. DOI: 10.1023/A:1024840405535

  15. Ross K.W. Multiservice Loss Models for Broadband Telecommunication Networks, Springer Verlag, 1995, 343 p.

  16. Fedorov V.V. Teoriya optimal'nogo eksperimenta (Theory of optimal experiment), Moscow, Nauka, 1971, 312 p.

  17. Fedorov V.V. Kibernetika, 1975, no. 1, pp. 124-130.

  18. Knyazeva M.V. Izvestiya YuFU. Tekhnicheskie nauki, 2010, no. 7 (108), pp. 78-84.

  19. Blazewicz J., Lenstra J., Kan A. Scheduling Subject to Resource Constraints: Classification and Comlexity, Discrete Applied Mathematics, 1983, vol. 5, pp. 11–24. DOI: 10.1016/0166-218X(83)90012-4

  20. Ovakimyan D.N., Zelenskii V.A., Kapalin M.V., Ereskin I.S. Trudy MAI, 2023, no. 132. URL: https://trudymai.ru/eng/published.php?ID=176849

  21. Minakov E.P., Aleksandrov M.A., Danilyuk B.A., Verbin A.V. Trudy MAI, 2023, no. 132. URL: https://trudymai.ru/eng/published.php?ID=176858

  22. Akentyev A.S., Fokina A.A., Makarov D.K. Trudy MAI, 2023, no. 130. URL: https://trudymai.ru/eng/published.php?ID=174597. DOI:10.34759/trd-2023-130-02

  23. Demeulemeester E.L., Herroelen W.S. Project Scheduling: A Researh Handbook, Department of Applied Economics Katholieke Universiteit, Leuven, 2002, 712 p.

  24. Orkin V.V. T-Comm Telekommunikatsii i transport, 2019, vol. 13, no. 2, pp. 52-59.

  25. Popov D.G. Svidetel'stvo o gosudarstvennoi registratsii programmy dlya EVM № 2023663452, 2023, 07.07.2023.


Download

mai.ru — informational site MAI

Copyright © 2000-2024 by MAI

 Вход