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Journal Information-measuring and Control Systems №11 for 2014 г.
Article in number:
The algorithm of antijamming of guidance system of unmanned aircraft from jump noisy signal, based on probabilistic approximation with beta-distribution
Keywords: guidance systems of UAV systems with random jump structure recognition and estimation of Markov process jump noisy signal Bayesian information processing two-moment parametrical approximations beta distribution
Authors:
V. A. Bukhalev - Dr.Sc. (Eng.), Professor, CJSC «Moscow Research Television Institute» (МНИТИ). E-mail: vadim.bukhalev@yandex.ru
N. N. Vilkova - Ph.D. (Eng.), CJSC «Moscow Research Television Institute» (МНИТИ). E-mail: mniti@mniti.ru
B. L. Shapiro - Research Scientist, CJSC «Moscow Research Television Institute» (МНИТИ). E-mail: mniti@mniti.ru
S. P. Pryadkin - Ph.D. (Eng.), Associate Professor, JSC «Radiotechnical Institute named after Academician A.L. Mintz» (Moscow). E-mail: pryadkin.serg@gmail.com
V. A. Boldinov - Ph.D. (Eng.), Associate Professor, Moscow Aviation Institute (National Research University). E-mail: viktorboldinov@mail.ru
Abstract:
The article covers the problem of constructing optimal algorithms for nonlinear filtering on the basis of methods of Markov theory of estimation of random processes. One of the most effective methods of setting organizational interference of information-management systems is a sudden, sharp and covert changing basic settings interference. Algorithms of antijamming of modern information management systems can detect interference and to estimate their parameters, but the sudden change of these parameters leads to transitional processes in the system and thereby reduces the accuracy and efficiency of its work. In this regard, algorithmic noise protection is build on the basis of the theory of systems with random jump structure in the class of Markov mathematical models using Bayesian information processing. As a result, this recurrent algorithms for the recognition of structure and assessment of signals are well implemented in modern computers. These algorithms can be divided into two categories: optimal and approximately optimal. Optimal algorithms allow us to get a posteriori distribution law of a state vector of the system, which consists of a structure of the state vector and the vector of the phase coordinates. Approximately optimal algorithms allow us to find a posteriori probability of a state structure, the mean of distribution (MD) and the covariance matrix (CM) of the error in estimating the vector of the phase coordinates without finding a posteriori distribution law. One of the most effective methods of constructing approximate optimal algorithms is the method of double-moment parametric approximation. The essence of this method consists in approximate replacement of unknown a posteriori probability densities of the state vector of known functions depending on the conditional a posteriori MD and CM under fixed conditions of the structure. In this article the problem of synthesis of algorithm for approximate optimal filtering of the signal in the conditions of intermittent interference is considered. The algorithm is based on the approximation of the unknown conditional a posteriori distribution laws with beta distributions with unknown parameters. These options conditional a posteriori MD and CM of the estimated signal with fixed states of patterns. The result is a closed system of recurrence equations for a posteriori probabilities of state structures, municipality and the signal CM, convenient from the point of view of computer realization and well adaptive to changes of the parameters of interference. In accordance with the algorithm proposed in the article a filter for a signal under random intermittent interference is development. Simulation results are presented in Matlab system. Algorithm adapts to changes of the parameters of the useful signal and interference, as the parameters of its blocks classifier, filter and the unit of estimation of dispersion, dependent on the evidence of the indicator patterns and measuring signal.
Pages: 37-48
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