S.M. Vyaznikov – Post-graduate Student, nibingiliat@mail.ru​​​​​​​
A.A. Chizhov – Dr.Sc.(Eng.), Associate Professor, Deputy Head, rtshouse@mail.ru

A method for resolving group concentrated air objects over a range with quasi-parallel verification of hypotheses about their number in active radar systems is considered. An approach to reduce computational costs at one of the most computationally intensive stages of processing echo signals of group concentrated air objects is given. It is shown that the quasi-parallel verification of hypotheses is possibly at the expense of the properties of a signal error matrix, as well as using repeated operations in the objective function computation. Estimates of the computational efficiency of the original and proposed algorithms are presented.

Quite often, in practice, there is a problem of resolving several air objects located within the pulse volume of the radar. For standard signal processing methods (correlation, filter, or correlation-filter), the radar capabilities for resolving concentrated groups of air objects in this case are limited by the known Rayleigh and Woodward criteria, which significantly reduces their functionality. It is possible to increase the radar resolution using super-Rayleigh resolution (super-resolution) methods. In comparison with the known methods of superresolution, higher energy efficiency is achieved when using the projection method for solving the inverse problem in a radar channel with scattering, developed at the Department of radar equipment of the Russian Federation Armed Forces Air Defense Military Academy (Smolensk, Russia). An important practical application of the super-resolution projection method is to estimate the actual number and parameters of individual air objects in a concentrated group. Based on the PM, a number of methods for resolving concentrated groups of air objects in the airspace survey radar are proposed. These methods are effective for typical signal-to-noise ratios for an air space survey radar, and can also be implemented using the output of standard correlation, filter, or correlation-filter signal processing schemes with typical noise protection systems. One of the limitations on the possibility of technical implementation of projection methods of signal processing is a sharp increase in computing costs with an increase in the number of objects allowed. Estimation of the configuration of a portrait of a concentrated group of air objects is the main labor-intensive procedure in the known method of super-resolution in range, the computational cost of which depends on the dimension and detail of the estimated radar portrait of a concentrated group of air objects. This fact is associated with a significant number of matrix operations when calculating the quadratic form, caused by the need to consistently test hypotheses about the quantitative composition of a concentrated group of air objects. In addition, when testing each hypothesis, it is necessary to iterate through all possible test positions of individual air objects in the parameter field (multigrid approach). Given the limited performance of domestic FPGAs, it will be quite difficult to process the echo signal of a concentrated group of air objects in the original way in real time. In addition, if the analysis area is highly detailed, a significant amount of memory is required to store the inverse mismatch matrices. These disadvantages impose restrictions on the maximum dimension of the hypothesis about the quantitative composition of concentrated groups of air objects and on the maximum number of processed echo signals of concentrated groups of air objects, which significantly reduces the ability of the airspace survey radar to resolve concentrated groups of air objects. It is possible to reduce computing costs and the amount of memory required by implementing a new signal processing method that is optimized to reduce computing costs. From the analysis of the assessment configuration of the portrait, as well as features of the structure matrix of the error signal shows that the signal processing there are some recurring transactions, which can be used for quasiparallel test hypotheses about the quantitative composition of groups of concentrated air targets. This approach to optimizing computational costs has reduced computational costs by more than an order of magnitude. The proposed super-resolution method will reduce the processing time of echo signals of concentrated groups of air objects by reducing the number of operations in the procedure for evaluating the configuration of the radar portrait, thereby ensuring the processing of the maximum possible number of echo signals of concentrated groups of air objects, provided that the FPGA computing power is limited.

Vyaznikov S.M., Chizhov A.A. Projection method for range resolution of group concentrated aerial objects with quasi-parallel verification of hypotheses about their numerical strength in active radars. Electromagnetic waves and electronic systems. 2020. V. 25. № 5. P. 79−91. DOI: 10.18127/j15604128-202005-08. (in Russian)

1. Voennaya operatsiya NATO v SRYu «Soyuznaya Sila» (24.03.1999 – 10.06.1999). Informatsionnaya spravochnaya sistema (ISS) «Voiny i vooruzhennye konflikty». Rezhim dostupa: http://10.120.58.200. (in Russian)
2. Deistviya aviatsii v lokalnykh voinakh. http://www.zvo.su/VVS/deystviya-aviacii-v-lokalnyh-voynah. (in Russian)
3. Aktualnye problemy i perspektivy razvitiya grazhdanskoi aviatsii Rossii. http://if-mstuca.ru/konferentciia/images/ Sborniki/sbornik_NPK_3−5.12.2013.pdf. (in Russian)
4. Relei (Dzh. Strett). Volnovaya teoriya sveta. M.–L: GTTI. 1940. 208 s. (in Russian)
5. Vudvord F.M. Teoriya veroyatnostei i teoriya informatsii s primeneniyami v radiolokatsii. M.: Sovetskoe radio. 1955. 128 s. (in Russian)
6. Pod druzhestvennym     ognem:   pochemu v             Sirii          byl           sbit         rossiiskii Il-20.  https://www.rbc.ru/politics/18/09/2018/ 5ba0d02f9a79476049e28e05. (in Russian)
7. Bodenskaya tragediya. https://russian.rt.com/world/article/404995-tragedia-bodenskoe-ozero. (in Russian)
8. Chizhov A.A. Metod razresheniya gruppovykh sosredotochennykh tselei. Radiotekhnika. 2009. № 10. S. 4−12. (in Russian)
9. Abramenkov V.V. Izmerenie koordinat radiolokatsionnykh tselei metodami mnogosignalnoi radiolokatsii. Smolensk: VU VPVO VS RF. 2002. 87 s. (in Russian)
10. Vyaznikov S.M. Analiz i otsenka effektivnosti perspektivnykh sposobov sverkhrazresheniya vozdushnykh ob’ektov v radiolokatorakh. Voennyi inzhener. 2019. № 2(12). S. 14−25. (in Russian)
11. Chizhov A.A. Sverkhrazreshenie i integralnoe uravnenie Fredgolma pervogo roda. M.: Si-press. 2015. 200 s. (in Russian)
12. Vasilevskii Yu.V., Olshanskii M.A. Kratkii kurs po mnogosetochnym metodam i metodam dekompozitsii. M.: MGU. 2007. 103 s. (in Russian)
13. Guskov Yu.N., Zhiburtovich N.Yu., Abramenkov V.V., Klimov S.A., Savinov Yu.I., Chizhov A.A. Sposob obnaruzheniya gruppovoi tseli. Patent na izobretenie № 2293349 po zayavke № 2005115054. Prioritet ot 18.05.05. Opublikovano 10.02.2007. (in Russian)
14. Kurochkin A.N. Sposob razresheniya gruppovykh sosredotochennykh tselei po dalnosti.. Radiotekhnicheskie i telekommunikatsionnye sistemy, 2011. № 2. S. 42–48. (in Russian)
15. Maksfild M.K. Proektirovanie na PLIS. Arkhitektura, sredstva i metody. M.: Izdatelskii dom «Dodeka-XXI». 2007. 408 s. (in Russian)
16. Rabinovich Z.L., Ramanauskas V.A. Tipovye operatsii v vychislitelnykh mashinakh. K.: Tekhnika. 1980. 264 s. (in Russian)
17. Raigorodskii A.M. Kombinatorika i teoriya veroyatnostei. Dolgoprudnyi: Intellekt. 2013. 104 s. (in Russian)
18. Novye rossiiskie PLIS. https://www.soel.ru/novosti/2019/novye_rossiyskie_plis/. (in Russian)