DOI:https://doi.org/10.15407/kvt210.04.003
Cybernetics and Computer Engineering, 2022, 4(210)
SUROVTSEV I.V., DSc (Engineering), Senior Researcher,
Head of the Digital Ecological Monitoring Systems Department
https://orcid.org/0000-0003-1133-6207
e-mail: igorsur52@gmail.com
KOMAR M.M., PhD (Engineering),
Deputy Director for Scientific and Organizational Work,
https://orcid.org/0000-0001-9194-2850
e-mail: nickkomar08@gmail.com
BOGACHUK Yu.P., PhD (Engineering),
Senior Researcher, Intelligent Control Department
https://orcid.org/0000-0002-3663-350X
e-mail: dep185@irtc.org.ua
SIERIEBRIAKOV A.K., PhD Student,
Researcher of Intelligent Control Department
https://orcid.org/0000-0003-3189-7968
e-mail: sier.artem1002@outlook.com
BABAK O.V., PhD (Engineering),
Senior Researcher of the Digital Ecological Monitoring Systems Department,
https://orcid.org/0000-0002-7451-3314
e-mail: dep115@irtc.org.ua
International Research and Training Center for Information Technologies and Systems of the National Academy of Sciences of Ukraine and the Ministry of Education and Science of Ukraine.
40, Acad. Glushkov av., Kyiv, 03187, Ukraine.
RECOGNITION OF THE TYPE OF MARINE SHIP BASED ON COMPARISON WITH NORMALIZED REFERENCE PARAMETERS OF RADIOLOCATION SIGNALS
Introduction. The problem of marine ship types recognition remains relevant because it primarily focuses on the safety of sea and inland navigation. The basis of the identification of the type of marine ship is the use of training samples – a set of reference normalized parameters of mathematical models of radar portraits of reflected signals, recorded in the database, for which the type of ship is reliably known.
The purpose of the paper is to develop a method for recognizing the type of surface marine ship by comparing the parameters of the radar portrait of the reflected signal of the radar object with the reference parameters of the signals of mathematical models of known types of marine ships.
Methods. The recognition method is based on comparison of the normalized parameters of the radar signal of the object with the normalized parameters of the mathematical models of the database references through a full search, after which a decision is made in favor of the type of marine ship for which the overall measure of inconsistency or the identification criterion is minimal. The identification criterion is the sum of dimensionless features, which are a measure of similarity in the chosen metric of the parameters regarding reference object.
Results. Testing of the developed recognition method on examination samples made it possible to identify the type and real orientation angle of the ship at the level of 83%, as well as to identify the types and recognize the orientation angles of marine ships at the level of 96%.
Conclusions. The new method of recognition of the type of marine ship is characterized by the use of insignificant computing power, high speed of analysis, compactness of the reference database, high reliability and accuracy of recognition. Determination of auxiliary alternative values of the identification of the type and orientation angle of the ship helps in the dynamic mode of observation to statistically specify the characteristics of the recognition of the ship. The developed method of recognizing the type of ship can be used in the military sphere, its use in radar systems will improve the safety of sea and inland navigation.
Keywords: recognition method, identification, type of marine ship, radar portrait of reflected signal.
REFERENCES
1 Vasiliev V.I. Recognizing systems. Directory. K.: Naukova Dumka. 1983, 422 p. (in Russian).
2 Vasilyev V.I., Surovtsev I.V. Inductive methods for pattern detection based on reduction theory. Control System and Computers. 1998, N 5, pp. 3-13 (in Russian).
3 Xinglong Liu, Yicheng Li, Yong Wu, Zhiyuan Wang, Wei He and Zhixiong Li. A Hybrid Method for Inland Ship Recognition Using Marine Radar and Closed-Circuit Television. J. Mar. Sci. Eng. 2021, 9, 1199.
https://doi.org/10.3390/jmse9111199
4 Ma F., Chen Y.W., Yan X.P., Chu X.M., Wang J. A novel marine radar targets extraction approach based on sequential images and Bayesian Network. Ocean. Eng. 2016, 120, 64-77.
https://doi.org/10.1016/j.oceaneng.2016.04.030
5 Misovi’c D.S., Mili’c S.D., Ðurovi’c Ž.M. Vessel detection algorithm used in a laser monitoring system of the lock gate zone. IEEE Trans. Intell. Transp.Syst. 2015, 17, 430-440.
https://doi.org/10.1109/TITS.2015.2477352
6 Liu, Yan-sen, Wang Yang, and Xue-Meng Yang. Acoustic spectrum and signature analysis on underwater radiated noise of a passenger ship target based on the measured data. International Conference on Signal Processing Systems, 2019, Chengdu, China.
https://doi.org/10.1117/12.2559664
7 https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11384/113840H/Acoustic- spectrum-and-signature-analysis-on-underwater-radiated-noise-of/10.1117/12.2559664.pdf
8 Zhu C., Seri S.G., Mohebbi-Kalkhoran H. et al. Long-range automatic detection, acoustic signature characterization and bearing-time estimation of multiple ships with coherent hydrophone array. Remote Sensing, 2020. 12(22), 3731. https://www.mdpi.com/2072-4292/12/22/3731/pdf.
https://doi.org/10.3390/rs12223731
9 Scarafoni Daniel et al. Automatic target recognition and geo-location for side scan sonar imagery.” The Journal of the Acoustical Society of America 141, 2017. № 5. 3925-3925.
https://doi.org/10.1121/1.4988877
10 Volkov O.Ye., Taranukha V.Yu., Linder Ya.M. et al. Acoustic monitoring technology, detection and localization of objects in a controlled space. Control Systems and Computers. 2020. № 4. P. 35-43 (in Ukrainian).
https://doi.org/10.15407/csc.2020.04.035
11 Volkov O.Ye., Taranukha V.Yu., Linder Ya.M., Komar M.M., Volosheniuk D.O. Devising an acoustic method for investigation of a complex form object parameters. Cyb. and Comp. Eng. 2021. N 4 (206). 39-53.
https://doi.org/10.15407/kvt206.04.039
12 Shirman Y.D., Gorshkov S.A., Leshchenko S.P., Orlenko V.M., Sedyshev S.Y., Sukharevskiy O.I. Computer Simulation of Aerial Target Radar Scattering, Recognition, Detection, and Tracking. Boston – London: Artech house, 2002, 294 p.
13 Molchanov P., Totsky A., Egiazarian K., Leshchenko S., Jarabo-Amores Pilar M. Classification of Aerial Targets by Using Bicoherence-Based Features Extracted from Micro-Doppler Contributions. IEEE Transaction on aerospace and electronic systems. 2014. № 2(50). 1455-1467.
https://doi.org/10.1109/TAES.2014.120266
14 Leshchenko S. The recognition quality effect of speed and aspect angle measurement errors using high range resolution profiles for aerial objects. Science and Technology of the Air Force of Ukraine. 2019. no 4(60). P. 23-30. (in Ukrainian).
15 Voinov, S.; Krause, D.; Schwarz, E. Towards automated vessel detection and type recognition from VHR optical satellite images. In Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 22-27 July 2018; 4823-4826.
https://doi.org/10.1109/IGARSS.2018.8519121
16 Solmaz, B.; Gundogdu, E.; Yucesoy, V.; Koç, A.; Alatan, A.A. Fine-grained recognition of maritime vessels and land vehicles by deep feature embedding. IET Comput. Vis. 2018, 12, 1121-1132.
https://doi.org/10.1049/iet-cvi.2018.5187
17 Youssef N.N. Radar cross section of complex targets. Proceedings of the IEEE. 1989. Vol. 77, Issue 5. 722-734.
https://doi.org/10.1109/5.32062
18 Ting, C., Wei, G., & Bing, S. (2011, July). A new radar emitter recognition method based on pulse sample figure. In Fuzzy Systems and Knowledge Discovery (FSKD), 2011 Eighth International Conference on (Vol. 3, pp. 1902-1905). IEEE
https://doi.org/10.1109/FSKD.2011.6019818
19 Petrova N., Jordanova I., Roeb J. Radar emitter signals recognition and classification with feedforward networks. Procedia Computer Science. N 22 (2013), 1192-1200.
https://doi.org/10.1016/j.procs.2013.09.206
20 Khrychov V.S., Legenky M.M. Facet model of an object of complex shape for the calculation of electromagnetic scattering. Bulletin of V.N. Karazin Kharkiv National University. Radiophysics and Electronics Series, 2019. (28), P. 44-52 (in Ukrainian).
21 French A. Target recognition techniques for multifunction phased array radar. Computer Science. 2010. Doctoral thesis, UCL (University College London), 308 p.
22 Jiansheng F., Xiaohong D., Wanlin Y. Radar HRRP Recognition Based on Discriminant Information Analysis. Wseas Transactions on Information Science and Aapplications. – 2011. N 4(8), 185
23 Method for histogram digital filtration of chrono-potentiometric data: patent 96367, Ukraine: IPC (2006) G01N 27/48. Surovtsev I.V., Galimova V.M., Babak O.V.: a201005608; claimed 11.05.10; published 25.10.11, Bull. 20 (in Ukrainian).-201.
Received 25.08.2022