S.E. Mishchenko – Dr.Sc.(Eng.), professor, Leading Research Scientist, 

FSUE «RNIIRS» FRPC

V.V. Shatsky – Honored Inventor of RF, Ph.D.(Eng.), Associate Professor, Head of Bureau,  FSUE «RNIIRS» FRPC

L.V. Vinnik – Part-programming Engineer, 

FSUE «RNIIRS» FRPC

A.V. Litvinov – Head of Sector, 

FSUE «RNIIRS» FRPC

A.S. Pomysov – Head of Team, 

FSUE «RNIIRS» FRPC

Effective use of the amount of information received by the receiving digital antenna array in the form of time samples of the signals of its channels is one of the directions for the further improvement of radar systems and radio navigation. One of the possibilities of such use is the solution of the problem of superresolution of closely located objects or detail of the radar image.

The purpose of the article is to increase the resolution of the receiving digital antenna array based on the development of a method for detailing radar images and super-resolution. To achieve it, it is necessary to solve the problem of synthesizing the spatial distribution of sources, to test the effectiveness of the method based on the theoretical model and the results of experimental studies.

The solution of the problem of determining the complex amplitudes and angular coordinates of radiation sources includes three main stages. At the first stage, the measurement of the complex amplitudes of the signals in the channels of an elemental digital antenna array is carried out. For their measurement, the Fourier transform of the input signal sequence is performed and the complex amplitude of the signal harmonics at the carrier frequency is selected. This complex amplitude of the signal harmonic corresponds to the complex amplitude in the аnd channel. At the second stage, the number of points is selected from the condition with a large margin. The values of the normalized radiation pattern are calculated at these points. Recommendations on the selection and placement of these points are given. At the third stage, it is required to take into account all the features of the introduced objective function and find a solution that ensures its lowest value.

The implementation of the proposed method is considered when using a genetic algorithm as a directional search algorithm. The features of its application are indicated.

The performed theoretical and experimental studies confirmed the effectiveness of the method and the possibility of detailing the radar image with a resolution two times higher than the Rayleigh criterion. This result is achievable even under the influence of three or four sources of radiation.

1. Lagovskij B.A. Vosstanovlenie izobrazheniya gruppovoj czeli czifrovy’mi antenny’mi reshetkami // Antenny’. 2011. № 2(165). S. 40−46. (in Russian)
2. Shamsunder S., Giannakis G.B. Detection and parameter estimation of multiple nongaussian sources via higher order statistics // IEEE Trans. Signal process. 1994. V. 42. № 5. P. 1145−1155.
3. Nikel U. Algebraic formulation of Kumaression-Tufts superresolution method, showing relation to ME and MUSIC methods // IEE Proc. Pt.F. 1988. V. 135. № 1. P. 7−14.
4. Raty’nskij M.V. Adaptacziya i sverxrazreshenie v antenny’x reshetkax. M.: Radio i svyaz’. 2003. 200 s. (in Russian)
5. Litvinov O.S. Algoritm sverxrazresheniya po metodu Kejpona pri obrabotke signalov v adaptivnoj antennoj reshetke s neidentichny’mi kanalami // Antenny’. 2004. № 8−9(87−88). S. 72−79. (in Russian)
6. Lagovskij B.A. Sverxrazreshenie na osnove sinteza apertury’ czifrovy’mi antenny’mi reshetkami // Antenny’. 2013. № 6(193). S. 9−16. (in Russian)
7. Ashikhmin A.V., Pasternak Yu.G., Rembovskiy Yu.A., Fedorov S.M. Virtual antenna array theory and applications // American Journal of Electromagnetics and Applications. 2015. № 3(1) P. 1−11. Published online January 30, 2015. (http://www.sciencepublishinggroup.com/j/ajea), doi: 10.11648/j.ajea.20150301.11.
8. Markov G.T., Petrov B.M., Grudinskaya G.P. E’lektrodinamika i rasprostranenie radiovoln. M.: Sov. radio. 1969. 376 s. (in Russian)
9. Karpenko A.P.Sovremenny’e algoritmy’ poiskovoj optimizaczii. Algoritmy’, vdoxnovlenny’e prirodoj. M.: Izd. MGTU im. N.E’. Baumana. 2014. 446 s. (in Russian)
10. Nazarov A.V., Loskutov A.I. Nejrosetevy’e algoritmy’ prognozirovaniya i optimizaczii. SPb.: Nauka i texnika. 2003. 384 s. (in Russian)