B.B. Nikolenko1, D.I. Kuznetsov2, V.A. Podkovkin3, P.B. Popov4, A.N. Nikulina5

1-5 JSC “CNIRTI named after academician A.I. Berg” (Moscow, Russia)

1 bb.nikolemko@gmail.com; 2 utsy@mail.ru; 3 pva.podkovkin@gmail.com; 4 paborpop@gmail.com; 5 alex.post-x@yandex.ru

Currently, the tasks of radio-electronic surveillance using spacecraft are the most relevant in ensuring the defense capability of the Russian Federation. In the course of solving the problem of radio-electronic surveillance, the issue of classifying the detected signals. Classical classification methods, based on comparing the obtained data on detected stations with reference a priori-known station parameters, do not allow obtaining results with a high degree of reliability, so to solve this issue we turned to machine learning technologies. This paper describes the solution to the problem of recognizing radar stations by the radio technical parameters of signals received during radio-electronic monitoring (SEM) of the Earth, using machine learning methods, taking into account the possibility of signals from unknown radars appearing in the test sample. In the process of work, an analysis of existing machine learning methods for processing SEM information was carried out using a developed mathematical model, implemented in program code, using simulated test data. In order to probabilistically evaluate methods for recognizing types of radar stations (radars) as part of the analysis of SEM information in a complex signal-interference environment, modeling of the initial data, consisting of a set of pairs “classification object - class label” was carried out. The radar type number was chosen as the class label, and the radar signal, which can be described using three characteristics: frequency, period, and signal pulse duration, was chosen as the classification object. The mathematical model for generating the initial data includes the ability to add a normally distributed measurement error depending on the specified standard deviation (RMSD) for each of the characteristics. In addition, the possibility of using various machine learning methods to search for outliers in the test sample was considered, and a probabilistic assessment of the accuracy of their work was also carried out. As a result of the research, based on the results obtained, the two most preferred methods of radar recognition were selected, as well as the most promising method of filtering signals from unknown radars. The use of the developed algorithms makes it possible to significantly speed up the recognition of radars based on the radio technical parameters of the signal with a high degree of recognition reliability.

Nikolenko B.B., Kuznetsov D.I., Podkovkin V.A., Popov P.B., Nikulina A.N. Using artificial intelligence methods to recognize radar stations based on the radio technical parameters of signals with filtering signals from radar stations not taken into account in the training set. Radiotekhnika. 2024. V. 88. № 5. P. 15−27. DOI: https://doi.org/10.18127/j00338486-202405-02 (In Russian)

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