Application of integral transforms in algorithms for detecting signals against a background of noise under priori uncertainty using the mellin's transforms

Abstract

For the modern theory of detecting signals on background noise, the main task is to reduce the degree of freedom of threshold values of decision rules to unknown 'interfering' signal and noise parameters. A particularly difficult task is the creation of new methods for the effective detection of signals from background noise with an unknown correlation function under conditions of a priori uncertainty. The task of overcoming a priori uncertainty regarding the parameters of signals and noise is a classic problem that was once dealt with using Fourier transforms. But the emergence in recent years of complex signals has led to the need to create new methods for detecting signals against a background noise