Recognition and evaluation of linear system output signal under intermittent simulated interference

Electronics, Radio and Communications


Аuthors

Bukhalev V. A.1*, Boldinov V. A.2**, Pryadkin S. P.3***

1. Moscow research television Institute, 7a, str. Golyanovskaya, Moscow, 105094, Russia
2. Moscow Aviation Institute (National Research University), 4, Volokolamskoe shosse, Moscow, А-80, GSP-3, 125993, Russia
3. Radiotechnical Institute named after academician A.L. Mintz, 10-1, str. 8 March, Moscow, 127083, Russia

*e-mail: vadim.bukhalev@yandex.ru
**e-mail: viktorboldinov@mail.ru
***e-mail: pryadkin.serg@gmail.com

Abstract

The problem of recognition and estimation of signal of the object in imaging and radio guidance systems for unmanned aerial vehicles in conditions of alternating receive true and false signals is discussed in the paper. The aim of this work is to develop a robust algorithm for recognition and estimation of input signals under conditions where random intermittent interference with the spectral density close to the signal is acting. Transient processes occurring in the system due to sudden, unexpected and usually poorly observed changes jamming environment require some definite time for their recognition and response. Consequently, as the methodological basis of the algorithm recognition and estimation of the input signal is advisable to use the apparatus of the theory of systems with random jump structure (SRJS) in the class of Markov mathematical models, Bayesian information processing and method two-moment parametrical approximation (TMPA). The latter consists in approximation of the unknown laws of distribution of signal and interference by some known laws with unknown mathematical expectation and the covariance matrices that allows constructing the suboptimal algorithms, combining both accuracy and easiness of implementation in computational devices UAV. The retrieved robust algorithm of information processing, based on the theory of SRJS, using Bayesian approach and two-moment parametrical approximation of the probability density of the signal with Pearsons distribution law. Simulation results of algorithm work showed, that despite the close resemblance of the true signal and noise the presence of a number of erroneous meter readings and indicator patterns, signal estimation and evaluation of the structure are quite close to the estimated variables that confirms good quality of the proposed algorithm.

Keywords:

systems with random jump structure, Markov process, Bayesian information processing, parametrical approximation

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