Measurement data approximation by pseudo-random sequences
Alexey Viktorovitch Levenets, Ruslan Ivanovich Bazhenov, Natalya A. Chalkina, Olga Chuyko, Zoya V. Arkhipova · AIP conference proceedings · 2021
The article describes a method of background processing (reduction) of measurement data, which allows such data to be represented by a set of samples of a pseudo-random generator. The core of the method, the principal points, and some recommendations to implement it are given in the paper. The findings of the research of the suggested method on test signals are presented. They show its crucial operability, at least at an SNR value not lower than 3.0. The method proposed by the authors provides acceptable values of the backing up error up to 0.25%, but if a quite large amount of pseudo-random sequences is given. It is also shown that the data processed by this method has a size of approximately half than the input data. So moreover, the method makes redundancy compression of encoded data possible.