Mathematical models of cross-correlated and uncorrelated Gaussian noise jamming from external sources

V. P. Riabukha; A. V. Semeniaka; Ye. A. Katiushyn

doi:10.3103/S0735272721030043

Authors

V. P. Riabukha Kvant Radar Systems Scientific Research Institute, Ukraine https://orcid.org/0000-0002-8607-9551
A. V. Semeniaka Kvant Radar Systems Scientific Research Institute, Ukraine https://orcid.org/0000-0002-1170-6151
Ye. A. Katiushyn Kvant Radar Systems Scientific Research Institute, Ukraine https://orcid.org/0000-0001-8200-7289

DOI:

https://doi.org/10.3103/S0735272721030043

Keywords:

Gaussian noise jamming, cross-correlated jamming, intercorrelated jamming, uncorrelated jamming from external source, mathematical model, linear phased antenna array, planar phased antenna array, input sample, correlation matrix, amplitude phase distribution

Abstract

This paper is devoted to the description of mathematical models of cross-correlated and uncorrelated Gaussian direct-noise jamming with uniform frequency spectrum produced by spatially-point external sources of noise radiation. They are designed for airspace control radars with spatially narrow-band linear equidistant phased antenna array (PAA) consisting of identical omnidirectional elements and also with planar or volumetric PAA. The use of the developed models allows researchers to generate the input (training) sample of the specified size using the pseudorandom number generators. This sample is formed by complex vectors, the components of which correspond to readings of complex amplitudes in spatial channels receiving at discrete time instants additive mixture of intrinsic noise and noise jamming from external sources with specified intensity and angular coordinates. The accuracy and adequacy of developed mathematical models have been proved analytically and checked by the method of mathematical experiments. The proposed models can be used for mathematical modeling in synthesis of adaptive radar protection systems with linear or planar and volumetric narrow-band PAA from Gaussian noise jamming and the analysis of their efficiency.

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