The Methods of Joint Signal Discrimination and Parameters Estimation in non-Gaussian Noise
Daniil Smirnov, Oleksandr Zorin, Elena Palahina, Valentyn Chepynoha, Artem Honcharov, Volodymyr Palahin · 2022
The use of the probability density distribution of random processes leads to certain difficulties in the implementation of signal processing algorithms, especially for processing non-Gaussian processes. One of the advanced approaches that allows describe non-Gaussian random processes is to use the moment and cumulant description of random variables. Based on this approach, two new methods of joint signal discrimination and parameter estimation are proposed. Nonlinear signal processing and taking into account the parameters of non-Gaussian processes can significantly improve the quality of signal processing compared to known classical methods.