КОМПЕНСАЦИОННЫЙ МЕТОД СЛЕПОГО ПОДАВЛЕНИЯ АДДИТИВНЫХ СТАЦИОНАРНЫХ ПОМЕХ С НЕГАУССОВЫМ РАСПРЕДЕЛЕНИЕМ

In a work a method of blind additive stationary non-gaussian disturbance suppression based on blind signal extraction at the output of the neural network according to a criteria (maximizing the kurtosis), and then compensation from the mixing with the signal is presented. The main advantage of this method is the possibility of disturbance suppression without a priori knowing of the signals and disturbances properties apart from some assumptions (belonging to the class of signal and disturbance distributions, the absence of a signal mixed with the disturbance at a certain time, the difference of statistical characteristics of signals and disturbances, some correlation properties of signals and disturbances, etc). Blind disturbance extraction algorithms based on the maximization of absolute and normalized kurtosis, are used; their advantages and weaknesses are identified. Modeling confirmed the efficiency of the compensation method achieving a signal-to-noise power ratio at the output of the neural network from 9 to 17 dB depending on the input signal-to-noise power ratio and used blind algorithms.