DETERMINATION OF TARGET COORDINATE ERRORS IN MULTI-POSITION RADAR USING GROUPS OF UNMANNED AIRCRAFT
Abstract
The article proposes and develops an algebraic method for determining the coordinates of targets and their errors as part of a group of unmanned aerial vehicles. The main assumptions of the developed model of the functioning of a group of unmanned aerial vehicles: The speeds of aircraft do not exceed the speed of sound in the air, and the speeds of targets do not exceed the first space were justified. The main assumptions of the model of operation of a group of unmanned aerial vehicles: the UAV speeds do not exceed the speed of sound in the air, and the target speeds do not exceed the first space one, are justified in the article. Qualitative estimates of the radar signal reception time for a given spatial error of the target coordinates were presented. The conditions for the number of aircraft in the group are formulated, which increase the accuracy of determining the location of the target in space. The various types of errors that arise when organizing the search for targets by a group of aircraft are analyzed. The issues of dependence of the resulting error in calculating the coordinates of the search target on the error in measuring the distance between the aircraft in the group and the target itself, depending on their mutual spatial orientation, are investigated. An algorithm has been developed, calculations and analysis of the results for this task have been carried out. The simulation is based on the proposed algorithm, taking into account random coordinates of the target in a fixed sector and taking into account random errors in the measured distance between a group of aircraft and the search object. The results of modeling the influence of the configuration of a group of unmanned aerial vehicles and the location of the target on the error in determining its coordinates are presented. An assessment was carried out to determine the coordinates of the goals and an error estimate of the proposed algebraic approach. The ways of further research are determined. The issues of estimating the amount of calculation for a large number of goals are considered. The scope and effectiveness of the proposed algorithm and method for solving the problem as a whole are determined.
References
1. Evtod'eva M.G., Tselitskiy S.V. Bespilotnye letatel'nye apparaty voennogo naznacheniya: tendentsii v sfere razrabotok i proizvodstva [Unmanned aerial vehicles for military purposes: trends in the field of development and production], Puti k miru i bezopasnosti [Paths to Peace and Security], 2019, No. 2 (57), pp. 104-111.
2. Abrosimov V.K. Gruppovoe dvizhenie intellektual'nykh letatel'nykh apparatov v antagonisticheskikh sredakh [Group movement of intelligent aircraft in antagonistic environments]. M.: Nauka, 2013, 168 p.
3. Antyukhov I.N., Levchenko E.M. Veroyatnostnaya model' obnaruzheniya ob"ektov, peremeshchaemykh s narusheniem tamozhennogo zakonodatel'stva [Probabilistic model for detecting objects moved in viola-tion of customs legislation], Akademicheskiy vestnik Rostovskogo filiala Rossiyskoy tamozhennoy akad-emii [Academic Bulletin of the Rostov branch of the Russian Customs Academy], 2024, No. 4 (57), pp. 19-22.
4. Zaytsev A.A., Kur'yan V.E., Levchenko E.M., Rodionov V.A. Nauchno-metodicheskie aspekty gruppovogo upravleniya bespilotnymi letatel'nymi apparatami [Scientific and methodological aspects of group control of unmanned aerial vehicles], «Fundamental'naya nauka – Voenno-Morskomu Flotu». T. 3: Mater. kruglogo stola v ramkakh VIII Mezhdunarodnogo voenno-morskogo salona (MVMS-2017). 27 iyunya 2017 g. ["Fundamental science - Naval 3. Materials of the round table within the framework of the VIII International Naval Salon (MVMS-2017). June 27, 2017]. Tver': NII «Tsentrpro-grammsistem», 2018, pp. 180-187.
5. Zaytsev A.A., Kur'yan V.E., Levchenko E.M., Sokolov S.V. Primenenie neyronnykh setey v zadachakh issledovaniya volnovykh yavleniy morskoy poverkhnosti [Application of neural networks in the tasks of studying wave phenomena of the sea surface], Metodologicheskie osnovy sozdaniya i primeneniya tekhnologiy i sistem dlya voenno-morskoy deyatel'nosti. Fundamental'naya nauka – Voenno-Morskomu Flotu: monografiya v dvukh tomakh [Methodological foundations of the creation and application of tech-nologies and systems for naval activities. Fundamental Science to the Navy: A monograph in two vol-umes]. Vol. 2. Saint Petersburg: Izd-vo SPbGEU, 2021, 123 p.
6. Leonov S.A. Radiolokatsionnye sredstva protivovozdushnoy oborony [Radar anti-aircraft defense sys-tems]. Moscow: Voennoe izdatel'stvo, 1988, pp. 122-133.
7. Boev S.F. Glaza i intellekt RKO. Vysokopotentsial'nye radiolokatsionnye stantsii: proshloe, nastoyash-chee i budushchee [Eyes and intelligence of the Red Army. High-potential radar stations: past, present and future], Voennyy parad [Military Parade], 2001, No. 5 (47), pp. 58.
8. Litvinov V.V. Sistemy raketno-kosmicheskoy oborony – garant bezopasnosti strany [Rocket and space defense systems are the guarantor of the country's security], Voennyy parad [Military Parade], 2001, No. 4 (46), pp. 88-89.
9. Aleshin B.S., Sukhanov V.L., Shibaev V.M., Shnyrev A.G. Sostoyanie del i perspektivy razvitiya kom-pleksov s bespilotnymi letatel'nymi apparatami v Rossii [The state of affairs and prospects for the devel-opment of complexes with unmanned aerial vehicles in Russia], Delovaya slava Rossii [Business glory of Russia], 2014, No. 3 (46), pp. 32-37.
10. Babushkin I.N., Kotkov A.S., Rastimeshin G.D. Intellekt gruppy BPLA: analiz poslednikh dostizheniy i tekushchee razvitie [Intelligence of the UAV group: analysis of recent achievements and current devel-opment], Vestnik novoy ERY: Sb. statey [Bulletin of the new ERA: Collection of articles], 2024, pp. 285-299.
11. Goncharenko V.I., Lebedev G.N., Kananadze S.S. [i dr.]. Zadacha tseleraspredeleniya dvizhushchikhsya ob"ektov pri ikh nablyudenii gruppoy bespilotnykh letatel'nykh apparatov [The task of target distribution of moving objects during their observation by a group of unmanned aerial vehicles], Neyrokomp'yutery i ikh primenenie: Sb. tezisov XXI Vserossiyskoy nauchnoy konferentsii (Moskva, 28 marta 2023 g.) [Neu-rocomputers and their application. Collection of abstracts of the XXI All–Russian Scientific Conference (Moscow, March 28, 2023)], 2023, pp. 55-57.
12. Voronov E., Repkin A., Kuslya A., Sychev S. Kompleksnyy algoritm obnaruzheniya, identifikatsii i tsel-eraspredeleniya dlya gruppy upravlyaemykh ob"ektov [Complex algorithm of detection, identification and target allocation for a group of controlled objects], Norwegian Journal of Development of the Inter-national Science, 2024, No. 126, pp. 41-50.
13. Stupnitskiy M.M., Myrova L.O., Korolev P.S. Roy BPLA – novaya paradigma primeneniya malo-razmernykh BPLA [Swarm of UAVs – a new paradigm for the use of small-sized UAVs], El-ektrosvyaz' [Telecommunications], 2023, No. 4, pp. 2-10.
14. Belyaev P.Yu., Zikratov I.A., Zikratova T.V., Neverov E.A. Ispol'zovanie pchelinogo algoritma dlya up-ravleniya royami BPLA pri monitoringe mestnosti [The use of a bee algorithm for controlling swarms of UAVs when monitoring terrain], Aktual'nye problemy infotelekommunikatsiy v nauke i obrazovanii (APINO 2023): Sb. nauchnykh statey. XII Mezhdunarodnaya nauchno-tekhnicheskaya i nauchno-metodicheskaya konferentsiya (Sankt-Peterburg, 28 fevralya – 01 marta 2023 g.) [Actual problems of infotelecommunications in science and education (APINO 2023). Collection of scientific articles. XII In-ternational Scientific, Technical and Scientific-methodological Conference (St. Petersburg, February 28 – March 01, 2023), 2023, Vol. 1, pp. 153-158.
15. Li C. Artificial Intelligence Technology in UAV Equipment, 2021 IEEE/ACIS 20th International Fall Conference on Computer and Information Science (ICIS Fall). Xi’an, China, 2021, P. 299-302. DOI: 10.1109/ICISFall51598.2021.9627359.
16. Varatharasan V., Rao A.S.S., Toutounji, E., et al. Target Detection, Tracking and Avoidance System for Low-cost UAVs using AI-Based Approaches, 2019 Workshop on Research, Education and Develop-ment of Unmanned Aerial Systems (RED UAS). Cranfield, UK, 2019, pp. 142-147. DOI: 10.1109/REDUAS47371.2019.8999683.
17. Zheng L., Ai P., and Wu Y. Building Recognition of UAV Remote Sensing Images by Deep Learning, IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium. Waikoloa, HI, USA, 2020, pp. 1185-1188. DOI: 10.1109/IGARSS39084.2020.9323322.
18. Zhang Y., McCalmon J., Peake A., et al. A Symbolic-AI Approach for UAV Exploration Tasks, 2021 7th International Conference on Automation, Robotics and Applications (ICARA). Prague, Czech Re-public, 2021, pp. 101-105. DOI: 10.1109/ICARA51699.2021.9376403.
19. Wang Y., Su Z., Zhang N., and Benslimane A. Learning in the Air: Secure Federated Learning for UAV-Assisted Crowdsensing, in IEEE Transactions on Network Science and Engineering. April-June 2021, Vol. 8, No. 2, pp. 1055-1069. DOI: 10.1109/TNSE.2020.3014385.
20. Kusyk J., Uyar M.U., Ma K., et al. AI and Game Theory based Autonomous UAV Swarm for Cyberse-curity, 2019 IEEE Military Communications Conference (MILCOM). Norfolk, VA, USA, 2019, pp. 1-6. DOI: 10.1109/MILCOM47813.2019.9020811.
21. Molina-Padrón N., Cabrera-Almeida F., Araña V., et al. Monitoring in Near-Real Time for Amateur UAVs Using the AIS, IEEE Access, 2020, Vol. 8, pp. 33380-33390. DOI: 10.1109/ACCESS.2020.2973503.
22. Zhang S., Wu X., Zhang G., et al. Analysis of intelligent inspection program for UAV grid based on AI, 2020 IEEE International Conference on High Voltage Engineering and Application (ICHVE). Xi'an, China, 2020, pp. 1-4. DOI: 10.1109/ICHVE49031.2020.9279634.
23. Kutakhov V.P., Meshcheryakov R.V. Printsipy formirovaniya modeli optimizatsii sistemy roboti-zirovannykh aviatsionnykh sredstv [Principles of forming a model for optimizing a system of robotic aviation facilities], Sb. trudov XIII Vserossiyskogo soveshchaniya po problemam upravleniya VSPU-2019 [Proceedings of the XIII All-Russian Meeting on management problems of VSPU–2019]. Mos-cow: Institut problem upravleniya im. V.A. Trapeznikova RAN, 2019, pp. 1211-1214.
24. Zharko E.F., Promyslov V.G., Iskhakova A.Yu. i dr. Kiberbezopasnost' bespilotnykh transportnykh sredstv. Arkhitektura. Metody proektirovaniya [Cybersecurity of unmanned vehicles. Architecture. De-sign methods]. Moscow: Radiotekhnika, 2021, 160 p.
25. Radilokatsionnye ustroystva [Radar devices], ed. by V.V. Grigorina-Ryabova. Moscow: Sovetskoe radio, 1970, 231 p.








