Determining the number of small-sized radars in a network with coherent signal processing for the detection of stealth aerial vehicles

Authors

DOI:

https://doi.org/10.15587/1729-4061.2024.306520

Keywords:

small-sized radar, aerial object detection, number of network elements, conditional probability of correct detection

Abstract

The object of this study is the process of determining the number of small-sized radars in the network when detecting stealth unmanned aerial vehicles. The main hypothesis of the study assumed that determining the optimal number of small-sized radars in the network will make it possible not to waste unnecessary resources of radars to detect stealth unmanned aerial vehicles.

The main stages of detection of a stealth unmanned aerial vehicle by a network of small-sized radars are:

– reception of the signal reflected from a stealth unmanned aerial vehicle by all small-sized radars of the network;

– coordinated filtering of incoming signals in each small-sized radar;

– compensation of phase shifts in each matched filter;

– coherent addition of output signals from each matched filter at the output of the receivers of each of the N small-sized radars performing reception;

– formation of a complex bypass at the output of the corresponding Doppler channel in each small-sized radar of the network;

– coherent processing of signals from all elements of the network of small-sized radars;

– detection of the output signal from the adder of coherent signals. At the same time, compensation for the random initial phase of signals reflected from a stealth unmanned aerial vehicle is also performed.

It has been established that the increase in the elements of the network of small-sized radars increases the value of the conditional probability of correct detection. Such an increase is more significant when the number of elements in the network of small-sized radars is increased to two or three. The gain in the signal/noise ratio when adding elements to the network of small-sized radars was evaluated. It was established that the optimal number of small-sized radars in a network with coherent signal processing when detecting stealth unmanned aerial vehicles is 2‒3 radars

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 Makoveichuk, Academician Y. Bugay International Scientific and Technical University

Doctor of Technical Sciences, Associate Professor

Department of Computer Sciences and Software Engineering

Ihor Butko, International Scientific and Technical University named after academician Yury Bugai

Doctor of Technical Sciences, Associate Professor

Department of Computer Sciences and Software Engineering

Mykhajlo Murzin, Ivan Kozhedub Kharkiv National Air Force University

PhD, Leading Researcher

Scientific Research Department of Air Force

Andrii Zvonko, Hetman Petro Sahaidachnyi National Army Academy

PhD, Senior Lecturer

Department of Rocket Artillery Armament

Anatolii Adamenko, Ivan Kozhedub Kharkiv National Air Force University

PhD, Senior Researcher

Scientific Research Department (Operational (Combat) Support of the Air Force)

Dmytro Bashynskyi, State Research Institute of Aviation

PhD, Leading Research

Research Department of Development and Modernization of Aviation Technology

Oleh Salnyk, Ivan Kozhedub Kharkiv National Air Force University

Senior Researcher

Scientific Research Department of the Air Force

Andrii Nyshchuk, Scientific-Research Institute of Military Intelligence

PhD, Senior Researcher

Scientific Research Department

Vladyslav Khudov, Kharkiv National University of Radio Electronics

PhD, Junior Researcher

Department of Information Technology Security

References

  1. Coluccia, A., Parisi, G., Fascista, A. (2020). Detection and Classification of Multirotor Drones in Radar Sensor Networks: A Review. Sensors, 20 (15), 4172. https://doi.org/10.3390/s20154172
  2. Yu, J., Liu, Y., Bai, Y., Liu, F. (2020). A double-threshold target detection method in detecting low slow small target. Procedia Computer Science, 174, 616–624. https://doi.org/10.1016/j.procs.2020.06.133
  3. Sentinel Radar. Available at: https://www.rtx.com/raytheon/what-we-do/land/sentinel-radar
  4. NASAMS anti-aircraft missile system. Available at: https://en.missilery.info/missile/nasams
  5. US Sentinel Radar Was Recorded in Ukraine. Available at: https://en.defence-ua.com/weapon_and_tech/us_sentinel_radar_was_recorded_in_ukraine-3357.html
  6. Bezouwen, J., Brandfass, M. (2017). Technology Trends for Future Radar. Available at: http://www.microwavejournal.com/articles/29367-technology-trends-for-future-radar
  7. Richards, M. A., Scheer, J. A., Holm, W. A. (Eds.) (2010). Principles of Modern Radar: Basic principles. Institution of Engineering and Technology. https://doi.org/10.1049/sbra021e
  8. Chernyak, V. (2014). Signal detection with MIMO radars. Uspehi sovremennoj radiojelectroniki, 7, 35–48.
  9. 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). https://doi.org/10.1109/ukrmico43733.2018.9047560
  10. Khudov, H. (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. https://doi.org/10.30534/ijeter/2020/66862020
  11. Multilateration (MLAT) Concept of Use. Edition 1.0 (2007). ICAO Asia and Pacific Office. Available at: https://www.icao.int/APAC/Documents/edocs/mlat_concept.pdf
  12. LORAN-C. Available at: https://skybrary.aero/articles/loran-c
  13. Rojhani, N., Shaker, G. (2024). Comprehensive Review: Effectiveness of MIMO and Beamforming Technologies in Detecting Low RCS UAVs. Remote Sensing, 16 (6), 1016. https://doi.org/10.3390/rs16061016
  14. Kalkan, Y. (2024). 20 Years of MIMO Radar. IEEE Aerospace and Electronic Systems Magazine, 39 (3), 28–35. https://doi.org/10.1109/maes.2023.3349228
  15. Neven, W. H., Quilter, T. J., Weedon, R., Hogendoorn, R. A. (2005). Wide Area Multilateration Wide Area Multilateration. Report on EATMP TRS 131/04 Version 1.1. National Aerospace Laboratory NLR. Available at: https://www.eurocontrol.int/sites/default/files/2019-05/surveilllance-report-wide-area-multilateration-200508.pdf
  16. 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. https://doi.org/10.1016/j.mcm.2012.03.004
  17. 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. https://doi.org/10.1109/tassp.1987.1165266
  18. 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). https://doi.org/10.23919/iccas50221.2020.9268431
  19. 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). https://doi.org/10.1109/ibcast51254.2021.9393232
  20. Wang, W. (2016). Overview of frequency diverse array in radar and navigation applications. IET Radar, Sonar & Navigation, 10 (6), 1001–1012. https://doi.org/10.1049/iet-rsn.2015.0464
  21. Li, Y. (2021). MIMO Radar Waveform Design: An Overview. Journal of Beijing Institute of Technology, 30 (1), 44–59. https://doi.org/10.15918/j.jbit1004-0579.2021.002
  22. 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. https://doi.org/10.46338/ijetae0821_04
  23. Khudov, H., Berezhnyi, A., Yarosh, S., Oleksenko, O., Khomik, M., Yuzova, I. et al. (2023). Improving a method for detecting and measuring coordinates of a stealth aerial vehicle by a network of two small-sized radars. Eastern-European Journal of Enterprise Technologies, 6 (9 (126)), 6–13. https://doi.org/10.15587/1729-4061.2023.293276
  24. Khudov, H., Yarosh, S., Kostyria, O., Oleksenko, O., Khomik, M., Zvonko, A. et al. (2024). Improving a method for non-coherent processing of signals by a network of two small-sized radars for detecting a stealth unmanned aerial vehicle. Eastern-European Journal of Enterprise Technologies, 1 (9 (127)), 6–13. https://doi.org/10.15587/1729-4061.2024.298598
  25. Chang, L. ZALA Lancet. Loitering munition. Available at: https://www.militarytoday.com/aircraft/lancet.htm
  26. Shin, S. ‐J. (2017). Radar measurement accuracy associated with target RCS fluctuation. Electronics Letters, 53 (11), 750–752. https://doi.org/10.1049/el.2017.0901
  27. Kishk, A., A., Chen, X. (Eds.) (2023). MIMO Communications - Fundamental Theory, Propagation Channels, and Antenna Systems. IntechOpen. https://doi.org/10.5772/intechopen.110927
Determining the number of small-sized radars in a network with coherent signal processing for the detection of stealth aerial vehicles

Downloads

Published

2024-06-28

How to Cite

Khudov, H., Makoveichuk, O., Butko, I., Murzin, M., Zvonko, A., Adamenko, A., Bashynskyi, D., Salnyk, O., Nyshchuk, A., & Khudov, V. (2024). Determining the number of small-sized radars in a network with coherent signal processing for the detection of stealth aerial vehicles. Eastern-European Journal of Enterprise Technologies, 3(9 (129), 37–45. https://doi.org/10.15587/1729-4061.2024.306520

Issue

Section

Information and controlling system