D.E. Boriskin¹, N.V. Gorbachev², I.D. Isaev³, A.N. Savelyev⁴

¹⁻⁴Federal State Autonomous Educational Institution for Higher Education «Bauman Moscow State Technical University (national research university)» (BMSTU, Moscow, Russia)

¹bde19k023@student.bmstu.ru, ²gorbachevnv@student.bmstu.ru, ³isaevid@bmstu.ru, ⁴savelyev.an@ bmstu.ru

Modern transport monitoring systems impose high requirements for detection reliability and accuracy of coordinate measuring and motion parameters of stationary and moving objects, identifying the need to increase the resolution of motion control radar systems in conditions of high traffic density.

One of the ways to solve the radar surveillance problem is the post-processing of digital radar image frames formed during the scan-period by circular view two-coordinate (azimuth – range) radars, which require high resolution, achievable by software methods.

Simulation and comparative analysis of basic superresolution methods (inverse filtering, Wiener filtering, Tikhonov method, Lucy-Richardson method, Split Bregman Algorithm) are performed. Conditions of their effective application for an artificially distorted image are estimated using the Pearson coefficient and the root-mean-square deviation of reconstructed image relative to the original one. Preferred areas of SNR for basic versions of objects superresolution algorithms on artificial digital frames have been identified.

For small signal-to-noise ratios (less than 0 dB), the Wiener and Tikhonov filters provide the most accurate estimate; for SNR values of more than 10 dB, the Split Bregman Algorithm and Richardson-Lucy deconvolution provide the most accurate estimate.

The need to optimize parameters of software algorithms to achieve the output effect involves the use of search methods, creating prerequisites for using machine learning to solve superresolution problems on digital radar image frames.

Boriskin D.E., Gorbachev N.V., Isaev I.D., Savelyev A.N. Comparative analysis of application conditions for basic two-coordinate radar superresolution algorithms. Information-measuring and Control Systems. 2026. V. 24. № 1. P. 11−22. DOI: https://doi.org/10.18127/j20700814-202601-02 (in Russian)

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