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Comparison of Interference Suppression Algorithms in Adaptive Antenna Arrays Based on Neural Network, Kalman and Traditional Control

The paper studies the problem of optimal reception of a useful signal by an adaptive antenna array (AAA) under the influence of non-stationary periodic interference in the frequency band. The characteristics of interference suppression in the AAA on amplitude-phase control are compared for the traditional direct matrix inversion algorithm, the algorithm based on a Kalman filter and neural network algorithms (MLP, RBF, LSTM). It is shown that the use of algorithms based on a recurrent neural network and based on a Kalman filter is reasonable and allows to get a gain in the average signal/(noise+interference) ratio (SNIR) at the AAA output in comparison with the traditional algorithm when solving this problem (for the simulated case by about 0.9 dB).