Analysis of nonstationary signals and fields with the use of enclosed semi-Markov processes

In this study, the possibility to describe the signals scattered by various physical objects such as underlying surfaces of land and sea, the segments of “clear sky,” and processes of various physical natures, such as fluctuations of the refractive index of the troposphere and electromagnetic and acoustic radiation of a lithospheric nature, by enclosed semi-Markov processes is investigated. This approach makes it possible to construct statistic models for a broad class of signals and processes. In some cases, statistics based on atomic functions and WA systems of the Kravchenko-Rvachev functions show the best results.     

Noise-induced hypersensitivity to weak alternating signals

An example of the helpful role of noise in information transmission processes is the well-known phenomenon of stochastic resonance. This letter examines another such example — parametric-noise-induced giant amplification of ultraweak signals in a system with on-off intermittency. Pis’ma Zh. éksp. Teor. Fiz. 67, No. 8, 592–596 (25 April 1998)     

Statistical synthesis of radiometric imaging formation in scanning radiometers with signal weight processing by Kravchenko windows

Statistical optimization of the radiometric images (RMIs) algorithms formation in scanning radiometers with weight correction of the antenna amplitude-phase distribution and synchronous sliding strobing of the received noise-like signal by a function describing the antenna pattern corrected by temporal Kravchenko windows is performed for the first time. The ambiguity function (AF) of the scanning radiometer, which determines the RMI quality, is found. It is established that the AF shape substantially depends on the amplitude field distribution (AFD) in the antenna. It is shown that the use of the AFD in the antenna in the form of weight functions (classic and Kravchenko) makes it possible to correct the AF shape and to increase the RMI quality. A simulation of the RMI formation algorithm is performed. It follows from the analysis of simulation data that the use of the weight Kravchenko functions provides higher accuracy of the RMI restoration compared with classic weight functions.