OPTIMIZATION OF JOINT SEARCH AND DETECTION OF OBJECTS IN TECHNICAL SURVEILLANCE SYSTEMS

Main Article Content

Hennadii Khudov
https://orcid.org/0000-0002-3311-2848
Kristina Tahyan
https://orcid.org/0000-0003-0087-9601
Viacheslav Chepurnyi
https://orcid.org/0000-0002-1341-1904
Irina Khizhnyak
https://orcid.org/0000-0003-3431-7631
Konstantin Romanenko
https://orcid.org/0000-0003-3107-3475
Artem Nevodnichii
https://orcid.org/0000-0003-4572-8807
Olexandr Yakovenko
https://orcid.org/0000-0002-7099-9011

Abstract

The subject matter of the article is joint search and detection of objects in technical surveillance system. The goal is solve the problem of optimizing joint search and detection of objects in technical surveillance system and develop a method for assessing the effectiveness of joint search and detection of objects for technical surveillance systems. Results. Introduced the current discrete area of view. The task of finding the optimal Bayes decision-making rule in the introduced current discrete area of view is posed and solved. The specified Bayes optimal decision rule is formulated. Proposes the efficiency estimation method of joint search and detection of objects for surveillance technical systems. An algorithm has been developed for calculating the unconditional probability of detecting an object of surveillance during a joint search and detection of objects in technical surveillance systems. Conclusions. Shown, that a joint search and detection of the objects of surveillance using a uniformly optimal search strategy provides a higher unconditional probability of the correct detection of the object of surveillance. In future research, it is necessary to assess the average time that is needed to detect the object of surveillance during the joint search and detection of objects and uniform distribution of the search potential of technical surveillance systems.

Article Details

How to Cite
Khudov, H., Tahyan, K., Chepurnyi, V., Khizhnyak, I., Romanenko, K., Nevodnichii, A., & Yakovenko, O. (2020). OPTIMIZATION OF JOINT SEARCH AND DETECTION OF OBJECTS IN TECHNICAL SURVEILLANCE SYSTEMS. Advanced Information Systems, 4(2), 156–162. https://doi.org/10.20998/2522-9052.2020.2.23
Section
Applied problems of information systems operation
Author Biographies

Hennadii Khudov, Ivan Kozhedub Kharkiv National Air Force University, Kharkiv

doctor of Technical Sciences, Professor, Head of Department

Kristina Tahyan, Ivan Kozhedub Kharkiv National Air Force University, Kharkiv

Research Associate

Viacheslav Chepurnyi, Ivan Kozhedub Kharkiv National Air Force University, Kharkiv

Department of Tactic and Military Disciplines

Irina Khizhnyak, Ivan Kozhedub Kharkiv National Air Force University, Kharkiv

Candidate of Technical Sciences, Lecturer of the Department of Mathematical and Software of ACS

Konstantin Romanenko, Ivan Kozhedub Kharkiv National Air Force University, Kharkiv

cadet

Artem Nevodnichii, Ivan Kozhedub Kharkiv National Air Force University, Kharkiv

cadet

Olexandr Yakovenko, Ivan Kozhedub Kharkiv National Air Force University, Kharkiv

cadet

References

Nguyen, H.V., Rezatofighi, H., Vo, Ba-Ngu and Ranasinghe, D. (2019), “Online UAV Path Planning for Joint Detection and Tracking of Multiple Radio-tagged Objects”, Proc. IEEE Transactions on Signal Processing, Sept., pp. 5365–5379, DOI: https://doi.org/10.1109/TSP.2019.2939076.

Nguyen, H.V., Chesser, M., Koh, L.P., Rezatofighi, S.H. and Ranasinghe, D.C. (2019), “TrackerBots: Autonomous UAV for Real-Time Localization and Tracking of Multiple Radio-Tagged Animals”, Journal of Area Robotics, vol. 36, no. 3, pp. 617–635, DOI: https://doi.org/10.1002/rob.21857.

Hoang, H.G. and Vo, B.T. (2014), “Sensor management for multi-target tracking via multi-Bernoulli filtering”, Automatica, vol. 50, no. 4, pp. 1135–1142, DOI: https://doi.org/10.1016/j.automatica.2014.02.007.

Wickens T.D. 92002), Elementary signal detection theory, Oxford University Press, New York, 2002, 277 p.

Li, J., Chu, X., He, W., Ma, F., Malekian, R. and Li, Z. (2019/0, “A Generalised Bayesian Inference Method for Maritime Sur-veillance Using Historical Data”, Symmetry, No. 11(2), 188, DOI: https://doi.org/10.3390/sym11020188.

Petrović, N., Jovanov, L., Pizurica, A. and Philips, W. (2008), “Object Tracking Using Naïve Bayesian Classifiers”, Advanced Concepts for Intelligent Vision Systems: 10th International Conference (ACIVS 2008), pp. 775–784. DOI: https://doi.org/10.1007/978-3-540-88458-3_70.

Khudov, H., Fedorov, A., Holovniak, D. and Misiyuk, G. (2018), “Improving the Efficiency of Radar Control of Airspace with the Multilateration System Use”, Intern. Scient.-Pract. Conf. Problems of Infocommunications. Science and Technology (PIC S&T), pp. 680-684, DOI: https://doi.org/10.1109/infocommst.2018.8632141.

Barniv, Y. (1985), “Dynamic programming solution for detecting dim moving targets”, IEEE Transactions on Aerospace and Electronic Systems, January 1985, Vol. AES-21, Iss. 1, pp. 144-156, DOI: https://doi.org/10.1109/TAES.1985.310548.

Khudov, H., Zvonko, A., Kovalevskyi, S., Lishchenko, V. and Zots, F. (2018), “Method for the detection of smallsized air objects by observational radars”, Eastern-European Journal of Enterprise Technologies, No. 2(92), pp. 61-68, DOI: https://doi.org/10.15587/1729-4061.2018.126509.

Khudov, H., Khizhnyak, I., Zots, F., Misiyuk, G. and Serdiuk, O. (2020), “The Bayes Rule of Decision Making in Joint Opti-mization of Search and Detection of Objects in Technical Systems”, IJETER, No. 8(1), pp. 7–12. DOI: https://doi.org/10.30534/ijeter/2020/02812020.

Khudov, H., Zvonko, A., Khizhnyak, I., Shulezko, V., Khlopiachyi, V., Chepurnyi, V. and Yuzova, I. (2020), “The Synthesis of the Optimal Decision Rule for Detecting an Object in a Joint Search and Detection of Objects by the Criterion of Maximum Likelihood”, IJETER, No. 8(2), 2020, p. 520–524. DOI: https://doi.org/10.30534/ijeter/2020/40822020.