E.E. Smirnov1, A.A. Pozdniakov2, M.S. Parshin3

1-3 Mozhaysky Military Space Academy (Saint Petersburg, Russia)

Currently, one of the topical issues arising in the functioning of radar stations for various purposes is the issue of tracking complex targets, namely the case of crossing the trajectories of several observation objects. When intersecting trajectories of objects, there is uncertainty in the presence of numerous elevations caused by reflections from a plurality of reflecting surfaces or areas of space, which leads to entanglement of trajectories, that is, the detected object is accompanied by a radar along the trajectory of another object. It is also possible to trace the second object along the trajectory of the first. This case is a special difficulty, as it leads to maintenance disruptions, loss of objects and their omission. At the same time, at the classification stage, an object can be assigned to a class to which it does not belong. Therefore, how to achieve a reliable classification of objects requires the development of methods for assessing its performance. To do this, a scientific and methodological apparatus for checking the quality of radar operation was developed (in which only trajectory information was analyzed at the first stage, and joint analysis of trajectory and polarization information was carried out at the second stage), which is a simulation model implemented in the software environment MathCad 15.0. The simulation results show that with an increase in the number of tracked objects and a decrease in the distance between them, the value of the classification quality indicator decreases. This indicates a contradiction between existing processing methods and classification quality requirements and indicates the need to develop new methods that provide a given quality indicator. A possible tool to resolve the contradiction may be the use of polarization information in order to ensure the required probability of correct classification of objects, namely, when identifying elevations and extrapolating trajectories at the stage of tracking objects of observation. In order to solve the problem, the initial data for the model of classification of objects were polarization scattering matrices, on the basis of which polarization parameters were calculated and object features were formed.

The results of the simulation show that the use of polarization information when tracking a large number of objects (from 10 trajectories and their intersection) provides the required level of classification quality for existing algorithms. The increase in the probability of correct classification ranged from 8% (at the edges of the radar viewing area) to 12% (in the center of the directional pattern).

Smirnov E.E., Pozdniakov A.A., Parshin M.S. Model of classification of observation objects under conditions of intersection of their motion paths based on joint analysis of trajectory and polarization information. Information-measuring and Control Systems. 2021. V. 26. № 3. P. 44−57. DOI: https://doi.org/10.18127/j20700814-202103-06 (in Russian)

1. Pozdnyakov A.A., Shatalov A.A., Shatalova V.A. Algoritm raspoznavaniya medlenno i bystro fluktuiruyushchikh tselei na fone pomekh mnogochastotnoi RLS s FAR. Vestnik vozdushno-kosmicheskoi oborony. 2019. № 2. S. 85−95. (in Russian)
2. Pozdnyakov A.A., Kalinin T.V., Shatalov A.A., KHodataev N.A. Osobennosti soprovozhdeniya manevriruyushchikh tselei s bystroizmenyayushcheisya skorostyu v radiotekhnicheskikh ustroistvakh. Vestnik Rossiiskogo novogo universiteta. Ser. «Slozhnye sistemy: modeli, analiz, upravlenie». 2018. № 3. S. 37−47. (in Russian)
3. Farina A., Studer F. Tsifrovaya obrabotka radiolokatsionnoi informatsii. Soprovozhdenie tselei. Per. s angl.. Pod red. A.N. Yureva. M.: Radio i svyaz. 1993. 322 s. (in Russian)
4. Leonov A.I. i dr. Modelirovanie v radiolokatsii. Pod red. A.I. Leonova. M.: Sov. radio. 1979. 264 s. (in Russian)
5. Manuilov Yu.S., Novikov E.A. Metodologiya sistemnykh issledovanii. SPb.: VKA imeni A.F. Mozhaiskogo. 2008. 159 s. (in Russian)
6. Alferev V.A. Metody imitatsionnogo modelirovaniya upravlyaemogo dvizheniya ballisticheskikh obieektov: monografiya. M.: OAO MAK «Vympel». 2016. 126 s. (in Russian)
7. Savrasov Yu.S. Algoritmy i programmy v radiolokatsii. M.: Radio i svyaz. 1985. 216 s. (in Russian)
8. Gorelik A.L., Barabash Yu.L., Krivosheev O.V., Epshtein S.S. Selektsiya i raspoznavanie na osnove lokatsionnoi informatsii. Pod red. A.L. Gorelika. M.: Radio i svyaz. 1990. 240 s. (in Russian)
9. Konovalov A.A. Osnovy traektornoi obrabotki radiolokatsionnoi informatsii. SPb.: Izd-vo SPbGETU «LETI». 2013. 164 s. (in Russian)
10. Fomin Ya.A., Tarlovskii G.R. Statisticheskaya teoriya raspoznavaniya obrazov. M.: Radio i svyaz. 1986. 264 s. (in Russian)
11. Kozlov A.I., Logvin A.I., Sarychev V.A. Polyarizatsiya radiovoln. Kn. 2. Radiolokatsionnaya polyarimetriya. M.: Radiotekhnika. 2007. 640 s. (in Russian)
12. Kanareikin D.B., Pavlov N.F., Potekhin V.A. Polyarizatsiya radiolokatsionnykh signalov. M.: Sov. radio. 1966. (in Russian)
13. Moskvin B.V. Teoriya prinyatiya reshenii. SPb.: VKA imeni A.F. Mozhaiskogo. 2005. 383 s. (in Russian)