O. S. Litvinov1, A. N. Zabelin2
1, 2 Bauman Moscow State Technical University (Moscow, Russia)

Adaptive antenna arrays (AAA) can be used to solve the problem of optimal reception of a useful signal in a complex signal-interference environment in modern radio-electronic systems. Adaptive beamforming algorithms in AAA make it possible to steer the main lobe of the radiation pattern towards the useful signal, and steer the nulls of the radiation pattern towards unwanted interferences.

To date, there is considerable interest in the problem of optimal signal reception by an adaptive antenna array under conditions of a nonstationary wideband periodic interference. Interference of this type is one of the most difficult for adaptation systems. If the modulation period of the interference is approximately equal to the tuning time of the AAA, then the AAA operates in a transient mode and cannot effectively suppress the interference.

The use of the Kalman filter in the AAA allows one to make a recursive assessment of the state of the signal-interference radio environment and suppress the frequency components of the interference in the general input signal. The Kalman filter can be used for multichannel preprocessing of input signals before calculating of weights for the AAA. It makes it possible to suppress periodic interference.

The purpose of the paper is to compare the suppression characteristics of the wideband periodic interference in the AAA based on the traditional algorithm without and with the Kalman filter.

The considered algorithm can be applied in wireless communication, radar and navigation systems to improve noise immunity, throughput capacity and some other characteristics.

A program has been developed in the Python programming language for modeling adaptive algorithms. The traditional algorithm was the sample matrix inversion algorithm based on the maximum SNIR criterion. The periodic interference in the experiment consisted of 2 fixed spatial components with sinusoidal amplitude modulation.

The numerical simulation showed that application of the Kalman filter in the AAA to predict the correlation matrix is relevant. The Kalman filter makes it possible to establish a quasi-stationary shape of the radiation pattern of the AAA in time. For the simulated scenario, the algorithm increases the average depth of suppression of spatial components of the periodic interference (by 5–7 dB) and the average SNIR (by about 0,9 dB), but slightly weakens the reception of the useful signal (by about 0,1 dB) compared to the traditional algorithm without the Kalman filter.

Litvinov O.S., Zabelin A.N. Comparison of the efficiency of interference suppression using the classical adaptation algorithms and using the Kalman filter. Antennas. 2022. № 1. P. 63–71. DOI: https://doi.org/10.18127/j03209601-202201-04 (in Russian)

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