Improving a method for detecting stealth aerial vehicles by using a network of two small-sized radars with decentralized information processing

Authors

DOI:

https://doi.org/10.15587/1729-4061.2024.302502

Keywords:

small-sized radar, aerial object detection, decentralized processing, conditional probability of correct detection

Abstract

The object of this study is the process of detecting stealth aerial vehicles by a network of two small-sized radars with decentralized signal processing. The main hypothesis of the study assumed that combining two small-sized radars into a network could improve the quality of detection of stealth aerial vehicles with decentralized signal processing.

The improved method for detecting a stealth aerial vehicle by a network of two small-sized radars with decentralized processing, unlike the known ones, provides for the following:

– each radar emits its own probing signal;

– each radar receives only its own signal;

– coordinated filtering in the reception system of each radar of its signal;

– quadratic detection of its signal in each radar;

– finding the sum of detected signals in each radar at the output of its matched filter;

– preliminary detection of the signal is carried out by each radar separately;

– in each range element, the signal is compared with the threshold level;

– when the threshold level in the range element is exceeded, such range element is assigned a value of one, otherwise – zero;

– the sequence of zeros and ones obtained in this way in each radar of the network is transmitted to the central processing point;

– at the central processing point, a decision is made about the presence or absence of a stealth aerial vehicle in the range element. Such a decision is made based on the results of the combined processing of binary sequences coming from the radars according to the "k out of m" criterion.

It was established that when detecting a stealth aerial vehicle by a network of two small-sized radars, decentralized information processing provides a higher value of the conditional probability of correct detection, by (19–26) % on average

Author Biographies

Hennadii Khudov, Ivan Kozhedub Kharkiv National Air Force University

Doctor of Technical Sciences, Professor, Head of Department

Department of Radar Troops Tactic

Andrii Zvonko, Hetman Petro Sahaidachnyi National Army Academy

PhD, Senior Lecturer

Department of Rocket Artillery Armament

Oleksandr Kostyria, Ivan Kozhedub Kharkiv National Air Force University

Doctor of Technical Sciences, Senior Research, Leading Researcher

Department of Radar Troops Tactic

Mykola Myroniuk, The National Defence University of Ukraine

Head of Research Department

Research Department of Aviation and Air Defense Applications

Dmytro Bashynskyi, State Research Institute of Aviation

PhD, Leading Researcher

Research Department of the Development and Modernization of Aviation Technology

Yuriy Solomonenko, Ivan Kozhedub Kharkiv National Air Force University

PhD, Deputy Head of Department

Department of Radar Troops Tactic

Artem Irkha, Scientific-Research Institute of Military Intelligence

PhD, Deputy Head of Center

Scientific and Methodical Center

Yevhen Dudar, Hetman Petro Sahaidachnyi National Army Academy

Deputy Head of Department

Department of Troop Training

Kostiantyn Snitkov, Hetman Petro Sahaidachnyi National Army Academy

PhD, Senior Lecturer

Department of Rocket Artillery Armament

Andrii Polishchuk, Hetman Petro Sahaidachnyi National Army Academy

Deputy Head of Department

Department of Rocket Artillery Armament

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Improving a method for detecting stealth aerial vehicles by using a network of two small-sized radars with decentralized information processing

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Published

2024-04-30

How to Cite

Khudov, H., Zvonko, A., Kostyria, O., Myroniuk, M., Bashynskyi, D., Solomonenko, Y., Irkha, A., Dudar, Y., Snitkov, K., & Polishchuk, A. (2024). Improving a method for detecting stealth aerial vehicles by using a network of two small-sized radars with decentralized information processing. Eastern-European Journal of Enterprise Technologies, 2(9 (128), 44–52. https://doi.org/10.15587/1729-4061.2024.302502

Issue

Vol. 2 No. 9 (128) (2024): Information and controlling system

Section

Information and controlling system

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Copyright (c) 2024 Hennadii Khudov, Andrii Zvonko, Oleksandr Kostyria, Mykola Myroniuk, Dmytro Bashynskyi, Yuriy Solomonenko, Artem Irkha, Yevhen Dudar, Kostiantyn Snitkov, Andrii Polishchuk

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