Using Software-Defined radio receivers for determining the coordinates of low-visible aerial objects

Authors

DOI:

https://doi.org/10.15587/1729-4061.2023.286466

Keywords:

low-visible aerial object, Software-Defined Radio, receiver, determination of coordinates, accuracy

Abstract

The object of this study is the process of determining the coordinates of low-visible aerial objects. The main hypothesis of the research assumed that the signals emitted by airborne systems of airborne objects that are not visible to radar stations have a greater power than the signal reflected from the airborne object. This, in turn, could improve the signal/noise ratio and, accordingly, the accuracy of determining the coordinates of low-visible aerial objects. It is suggested to use Software-Defined Radio receivers to receive such signals emitted by on-board systems of low-visible aerial objects.

It was established that the main sources of signals for Software-Defined Radio receivers are signals of command, telemetry, target channels, manual control channels, and satellite navigation. It was established that an additional distinguishing feature when determining the coordinates of low-visible aerial objects is the uniqueness of their spectra and spectrograms.

The method of determining the coordinates of low-visible aerial objects when using Software-Defined Radio receivers has been improved, which, unlike the known ones, involves:

– the use as signals for Software-Defined Radio of signal receivers of on-board equipment of low-visible aerial objects;

– the use of a priori coordinate values of a low-visible aerial object;

– conducting additional spectral analysis of signals of on-board systems of low-visible aerial objects.

The spectra and spectrograms of signals of on-board systems of aerial objects when using non-directional and directional antennas were experimentally determined. The experimental studies confirm the possibility of using the Software-Defined Radio receiver to receive signals from airborne equipment and improve the signal-to-noise ratio.

The accuracy of determining the coordinates of aerial objects when using Software-Defined Radio receivers was evaluated. A decrease in the error of determining plane coordinates by the Software-Defined Radio system of receivers compared to the accuracy of determining coordinates by the P-19 MA radar station was established by an average of 1.88–2.47 times, depending on the distance to the aerial object

Author Biographies

Hennadii Khudov, Ivan Kozhedub Kharkiv National Air Force University

Doctor of Technical Sciences, Professor, Head of Department

Department of Radar Troops Tactic

Oleksandr Kostianets, Ivan Kozhedub Kharkiv National Air Force University

PhD, Senior Lecturer

Department of Combat Use of Radar Troops Armament

Oleksandr Kovalenko, Central Ukrainian National Technical University

Doctor of Technical Sciences, Associate Professor

Department of Cybersecurity and Software

Oleh Maslenko, Scientific-Research Institute of Military Intelligence

PhD, Senior Researcher

Scientific Research Department

Yuriy Solomonenko, Ivan Kozhedub Kharkiv National Air Force University

PhD, Deputy Head of the Faculty of Educational and Scientific Work

Faculty of Radar-Technical Troops of Anti-Aircraft

References

  1. Erl, J. (2022). Sensing digital objects in the air: Ultraleap introduces new technology. Available at: https://mixed-news.com/en/sensing-digital-objects-in-the-air-ultraleap-introduces-new-technology/
  2. Sample, I. (2023). What do we know about the four flying objects shot down by the US? Available at: https://www.theguardian.com/world/2023/feb/13/what-do-we-know-about-the-four-flying-objects-shot-down-by-the-us
  3. Carafano, J. J. (2022). Rapid advancements in military tech. Available at: https://www.gisreportsonline.com/r/military-technology
  4. Stilwell, B. (2023). 4 Amazing Military Aviation Technologies We'll See in the Near Future. Available at: https://www.military.com/off-duty/4-amazing-military-aviation-technologies-well-see-near-future.html
  5. Globa, L., Dovgyi, S., Kopiika, O., Kozlov, O. (2022). Approach to Uniform Platform Development for the Ecology Digital Environment of Ukraine. Lecture Notes in Networks and Systems, 83–100. doi: https://doi.org/10.1007/978-3-031-16368-5_4
  6. Orlan-10 Uncrewed Aerial Vehicle (UAV). Available at: https://www.airforce-technology.com/projects/orlan-10-unmanned-aerial-vehicle-uav/#catfish
  7. Russia behind the UAV technology curve (2021). Available at: https://issuu.com/edrmag/docs/edr_58_-_web/s/12783061
  8. Chang, L. ZALA Lancet. Loitering munition. Available at: https://www.militarytoday.com/aircraft/lancet.htm
  9. Chopra, A. (2022). Next gen military technologies. Available at: https://www.sps-aviation.com/story/?id=3161&h=Next-Gen-Military-Technologies
  10. Wang, H., Cheng, H., Hao, H. (2020). The Use of Unmanned Aerial Vehicle in Military Operations. Lecture Notes in Electrical Engineering, 939–945. doi: https://doi.org/10.1007/978-981-15-6978-4_108
  11. Richards, M. A., Scheer, J. A., Holm, W. A. (2010). Principles of modern radar. Vol. I. Basic principles. Raleigh: SciTech Publishing, 924. doi: https://doi.org/10.1049/sbra021e
  12. Khudov, H., Zvonko, A., Kovalevskyi, S., Lishchenko, V., Zots, F. (2018). Method for the detection of small­sized air objects by observational radars. Eastern-European Journal of Enterprise Technologies, 2 (9 (92)), 61–68. doi: https://doi.org/10.15587/1729-4061.2018.126509
  13. Melvin, W. L., Scheer, J. A. (2013). Principles of modern radar. Vol. II. Advanced techniques. Raleigh: SciTech Publishing, 846. doi: https://doi.org/10.1049/sbra020e
  14. Melvin, W. L., Scheer, J. A. (2014). Principles of modern radar. Vol. III. Radar applications. Raleigh: SciTech Publishing, 820. doi: https://doi.org/10.1049/sbra503e
  15. Bezouwen, J., Brandfass, M. (2017). Technology Trends for Future Radar. Available at: https://www.microwavejournal.com/articles/29367-technology-trends-for-future-radar
  16. Lishchenko, V., Kalimulin, T., Khizhnyak, I., Khudov, H. (2018). The Method of the organization Coordinated Work for Air Surveillance in MIMO Radar. 2018 International Conference on Information and Telecommunication Technologies and Radio Electronics (UkrMiCo). doi: https://doi.org/10.1109/ukrmico43733.2018.9047560
  17. Khudov, H. et. al. (2020). The Coherent Signals Processing Method in the Multiradar System of the Same Type Two-coordinate Surveillance Radars with Mechanical Azimuthal Rotation. International Journal of Emerging Trends in Engineering Research, 8 (6), 2624–2630. doi: https://doi.org/10.30534/ijeter/2020/66862020
  18. Marpl-ml, S. L. (1990). Tsifrovoy spektral'niy analiz i ego. Mosocw: Mir, 584.
  19. Klimov, S. A. (2013). Metod povysheniya razreshayuschey sposobnosti radiolokatsionnykh sistem pri tsifrovoy obrabotke signalov. Zhurnal radioelektroniki, 1. Available at: http://jre.cplire.ru/jre/jan13/1/text.html
  20. Bhatta, A., Mishra, A. K. (2017). GSM-based commsense system to measure and estimate environmental changes. IEEE Aerospace and Electronic Systems Magazine, 32 (2), 54–67. doi: https://doi.org/10.1109/maes.2017.150272
  21. Neyt, X., Raout, J., Kubica, M., Kubica, V., Roques, S., Acheroy, M., Verly, J. G. (2006). Feasibility of STAP for Passive GSM-Based Radar. 2006 IEEE Conference on Radar. doi: https://doi.org/10.1109/radar.2006.1631853
  22. Willis, N. J. (2004). Bistatic Radar. IET. doi: https://doi.org/10.1049/sbra003e
  23. Lishchenko, V., Khudov, H., Tiutiunnyk, V., Kuprii, V., Zots, F., Misiyuk, G. (2019). The Method of Increasing the Detection Range of Unmanned Aerial Vehicles In Multiradar Systems Based on Surveillance Radars. 2019 IEEE 39th International Conference on Electronics and Nanotechnology (ELNANO). doi: https://doi.org/10.1109/elnano.2019.8783263
  24. Ruban, I., Khudov, H., Lishchenko, V., Pukhovyi, O., Popov, S., Kolos, R. et al. (2020). Assessing the detection zones of radar stations with the additional use of radiation from external sources. Eastern-European Journal of Enterprise Technologies, 6 (9 (108)), 6–17. doi: https://doi.org/10.15587/1729-4061.2020.216118
  25. Leshchenko, S., Kolesnik, O., Gricaenko, S., Burkovsky, S. (2017). Use of the ADS-B information in order to improve quality of the air space radar reconnaissance. Nauka i tekhnika Povitrianykh Syl Zbroinykh Syl Ukrainy, 3 (28), 69–75. Available at: http://nbuv.gov.ua/UJRN/Nitps_2017_3_11
  26. Khudov, H., Diakonov, O., Kuchuk, N., Maliuha, V., Furmanov, K., Mylashenko, I. et al. (2021). Method for determining coordinates of airborne objects by radars with additional use of ADS-B receivers. Eastern-European Journal of Enterprise Technologies, 4 (9 (112)), 54–64. doi: https://doi.org/10.15587/1729-4061.2021.238407
  27. LORAN-C. Available at: https://skybrary.aero/articles/loran-c
  28. Multilateration (MLAT) Concept of Use. Available at: https://www.icao.int/APAC/Documents/edocs/mlat_concept.pdf
  29. Neven, W. H., Quilter, T. J., Weedon, R., Hogendoorn, R. A. (2005). Wide Area Multilateration Report on EATMP TRS 131/04 Version 1.1. Available at: https://www.eurocontrol.int/sites/default/files/2019-05/surveilllance-report-wide-area-multilateration-200508.pdf
  30. Mantilla-Gaviria, I. A., Leonardi, M., Balbastre-Tejedor, J. V., de los Reyes, E. (2013). On the application of singular value decomposition and Tikhonov regularization to ill-posed problems in hyperbolic passive location. Mathematical and Computer Modelling, 57 (7-8), 1999–2008. doi: https://doi.org/10.1016/j.mcm.2012.03.004
  31. Schau, H., Robinson, A. (1987). Passive source localization employing intersecting spherical surfaces from time-of-arrival differences. IEEE Transactions on Acoustics, Speech, and Signal Processing, 35 (8), 1223–1225. doi: https://doi.org/10.1109/tassp.1987.1165266
  32. Khudov, H., Mynko, P., Ikhsanov, S., Diakonov, O., Kovalenko, O., Solomonenko, Y. et al. (2021). Development a method for determining the coordinates of air objects by radars with the additional use of multilateration technology. Eastern-European Journal of Enterprise Technologies, 5 (9 (113)), 6–16. doi: https://doi.org/10.15587/1729-4061.2021.242935
  33. Khudov, H., Yarosh, S., Droban, O., Lavrut, O., Hulak, Y., Porokhnia, I. et al. (2021). Development of a direct penetrating signal compensator in a distributed reception channel of a surveillance radar. Eastern-European Journal of Enterprise Technologies, 2 (9 (110)), 16–26. doi: https://doi.org/10.15587/1729-4061.2021.228133
  34. Oleksenko, O., Khudov, H., Petrenko, K., Horobets, Y., Kolianda, V., Kuchuk, N. et al. (2021). The Development of the Method of Radar Observation System Construction of the Airspace on the Basis of Genetic Algorithm. International Journal of Emerging Technology and Advanced Engineering, 11 (8), 23–30. doi: https://doi.org/10.46338/ijetae0821_04
  35. Ryu, H., Wee, I., Kim, T., Shim, D. H. (2020). Heterogeneous sensor fusion based omnidirectional object detection. 2020 20th International Conference on Control, Automation and Systems (ICCAS). doi: https://doi.org/10.23919/iccas50221.2020.9268431
  36. Salman, S., Mir, J., Farooq, M. T., Malik, A. N., Haleemdeen, R. (2021). Machine Learning Inspired Efficient Audio Drone Detection using Acoustic Features. 2021 International Bhurban Conference on Applied Sciences and Technologies (IBCAST). doi: https://doi.org/10.1109/ibcast51254.2021.9393232
  37. SHAHED-136 Loitering munition / Kamikaze-Suicide drone – Iran (2023). Available at: https://www.armyrecognition.com/iran_unmanned_ground_aerial_vehicles_systems/shahed-136_loitering_munition_kamikaze-suicide_drone_iran_data.html#google_vignette
  38. How drones are conquering the battlefield in Ukraine's war (2023). Available at: https://www.euronews.com/2023/06/06/how-drones-are-conquering-the-battlefield-in-ukraines-war
  39. Space, the unseen frontier in the war in Ukraine (2022). BBC News. Available at: https://www.bbc.com/news/technology-63109532
  40. NASAMS Air Defence System. Available at: https://www.kongsberg.com/kda/what-we-do/defence-and-security/integrated-air-and-missile-defence/nasams-air-defence-system/
  41. Fedorov, A., Holovniak, D., Khudov, H., Misiyuk, G. (2019). Method of Radar Adjustment with Automatic Dependent Surveillance Technology Use. 2019 IEEE International Scientific-Practical Conference Problems of Infocommunications, Science and Technology (PIC S&T). doi: https://doi.org/10.1109/picst47496.2019.9061245
  42. Byrne, J., Watling, J., Bronk, J., Somerville, G., Byrne, J., Crawford, J., Baker, J. (2022). The Orlan complex. Tracking the supply chains of Russia’s most successful UAV. Royal United Services Institute for Defence and Security Studies. Available at: https://static.rusi.org/SR-Orlan-complex-web-final.pdf
  43. Swiss Components For Cars and Electric Bicycles Were Found in russian Orlan-10 UAVs and Missiles (2023). Available at: https://en.defence-ua.com/industries/swiss_components_for_cars_and_electric_bicycles_were_found_in_russian_orlan_10_uavs_and_missiles-6267.html
  44. What is a Spectrogram? Available at: https://vibrationresearch.com/blog/what-is-a-spectrogram
  45. What is a Spectrogram? Available at: https://pnsn.org/spectrograms/what-is-a-spectrogram
  46. Eleron-3SV. Available at: https://robotrends.ru/robopedia/eleron-3sv
  47. Saybel', A. G. (1958). Osnovy teorii tochnosti radiotekhnicheskikh metodov mestoopredeleniya. Moscow: Oborongiz, 55.
  48. Khudov, H., Zvonko, A., Lisohorskyi, B., Solomonenko, Y., Mynko, P., Glukhov, S. et al. (2022). Development of a rangefinding method for determining the coordinates of targets by a network of radar stations in counter-battery warfare. EUREKA: Physics and Engineering, 3, 121–132. doi: https://doi.org/10.21303/2461-4262.2022.002380
  49. Ruban, I., Khudov, H., Makoveichuk, O., Butko, I., Glukhov, S., Khizhnyak, I. et al. (2022). Application of the Particle Swarm Algorithm to the Task of Image Segmentation for Remote Sensing of the Earth. Lecture Notes in Networks and Systems, 573–585. doi: https://doi.org/10.1007/978-981-19-5845-8_40
  50. Khudov, H., Makoveichuk, O., Khizhnyak, I., Oleksenko, O., Khazhanets, Y., Solomonenko, Y. et al. (2022). Devising a method for segmenting complex structured images acquired from space observation systems based on the particle swarm algorithm. Eastern-European Journal of Enterprise Technologies, 2 (9 (116)), 6–13. doi: https://doi.org/10.15587/1729-4061.2022.255203
  51. AIRSPY. Available at: https://airspy.com
  52. Apaydin, G., Sevgi, L. (2017). Radio Wave Propagation and Parabolic Equation Modeling. The Institute of Electrical and Electronics Engineers. doi: https://doi.org/10.1002/9781119432166
  53. P-19MA. Available at: http://uoe.com.ua/products/en/?id=0&pid=catalogue&language=eng&catalogue_id=515&type=content
  54. HackRF One SDR-transiver (1 MHts – 6 HHts) maksymalna komplektatsiya. Available at: https://radioscan.com.ua/ua/p1878031526-hackrf-one-sdr.html
Using Software-Defined radio receivers for determining the coordinates of low-visible aerial objects

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Published

2023-08-31

How to Cite

Khudov, H., Kostianets, O., Kovalenko, O., Maslenko, O., & Solomonenko, Y. (2023). Using Software-Defined radio receivers for determining the coordinates of low-visible aerial objects. Eastern-European Journal of Enterprise Technologies, 4(9 (124), 61–73. https://doi.org/10.15587/1729-4061.2023.286466

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Information and controlling system