Recognition and evaluation of the output signal of a nonlinear system in the conditions of intermittent interference simulation

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 aim of this work is to develop robust algorithm of recognition and estimation of input signals in conditions of action of random intermittent interference with the spectral density close to the signal. Transient processes occurring in the system due to the sudden, unexpected and usually poorly observed changes jamming environment, require some 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). TMPA technique consists in approximation of the unknown laws of distribution of a signal and interference by the known laws of unknown mathematical expectation and the covariance matrices that allows constructing the approximate optimal algorithms, combining well accuracy with the ease of implementation in computational devices of unmanned aerial vehicles.
Optimization of the algorithm of recognition and estimation of the useful signal is carried out using Bayesian approach and double-stage parametric approximation of a posteriori probability density signal beta distribution. Appropriate quality of the algorithm is proved by the results of modeling of its work, which 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.

Keywords:

systems with random jump structure, recognition and estimation of Markov process, Bayesian information processing, two-moment parametrical approximation, beta distribution

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