Yu.L. Nikolashin - Ph.D. (Eng.), General Director, PJSC "Inteltech" (St. Petersburg). E-mail: intelteh@inteltech.ru P.A. Budko - Ph.D. (Eng.), Professor, Leading Research Scientist, PJSC "Inteltech" (St. Petersburg). E-mail: budko62@mail.ru G.A. Zhukov - Ph.D. (Eng.), Associate Professor, PJSC "Inteltech" (St. Petersburg). E-mail: intelteh@inteltech.ru

A new method of processing of the messages accepted on radio channels of a decameter wave band, realized with use of the device of artificial neural networks, demanding large volume of parallel calculations which carrying out is problematic when using regular computing means is offeredin the article. Expediency of use of the device of artificial neural networks is caused by need of realization of volume computing procedures on neurocomputers in the presence of big massifs entrance, parallel to the arriving information, and also in connection with partial distortion of data on object or decision-making in the conditions of aprioristic uncertainty. It is characteristic in case of the information processing arriving against noise (natural and artificial hindrances) on the channel with variable parameters to which it is possible to carry a decameter radio channel. The tasks of increase in reliability of the accepted information due to creation of the automated radio lines, including with adaptation of the main characteristics of antenna subsystems and a hardware-software complex of communication to an elec-tromagnetic situation in a reception point are solved in the article. The assessment of efficiency of functioning of adaptive compensators of hindrances in the channel with variable parameters is presented. One of the ways of increasing of the efficiency of suppression of hindrances when using multichart digital antenna lattices in the conditions of a feding of a signal (hindrance) on the level (an azimuth and a corner of a place) by formation of a fan of static directional patterns with simultaneous reception and processing of the signals arriving from all channels of paths of the carried reception that allows to realize reception of information with the maximum relation a signal/hindrance at arrival of a hindrance and useful signal from any azimuths. Results of computer modeling on their formation for an eight-level ring antenna lattice with a radius of 10 meters, a step of change of a phase 300 and with a working frequency of 20 MHz are given. The analysis of the received results showed that when forming the of directional patterns will always be such which provides reception of a useful signal with simultaneous suppression of the hindrance coming from any azimuth (in coincident with an azimuth of arrival of a useful signal). Thus for achievement of size of suppression of a hindrance more than 40 dB will be required as show calculations to create about 360 directional patterns with a shift step in 10. In this case the most effective is the parallel analysis of signals with formation of the total message consisting of the blocks of the message which are optimum accepted from the directional patterns corresponding the. The block diagram of the hardware-software complex providing optimization of reception and processing of messages in the channel with variable parameters in the conditions of influence of hindrances in which resultant formation of the message is made with use of character-by-character weight majority addition on the basis of criterion of maximum likelihood is offered. The approximate necessary number in parallel of the carried-out calculations even at small quantity of antenna elements in diversity receptionat realization of the considered method makes more than 100 thousand that is problematic to realize in real time on regular computer systems and demands use of specialized neurocomputers. The offered method allows to carry out modernization existing and to create the new highly effective automated systems of a radio communication and radio monitoring with difficult algorithms of the functioning and information processing which is based on a number of the theorems defining the approximating properties of multilayered neural networks.

 

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