THE DEPENDENCE OF THE FALL OF THE SIGNAL POWER ON THE PARAMETERS OF THE FOREST ENVIRONMENT WHEN THE RADIO FREQUENCY OF FOREST MONITORING

Sannikov Sergey Petrovich, candidate of technical sciences, associate professor, sub-department of automation production processes, Ural State Forestry University (37 Siberian highway st., Ekaterinburg, Russia), ssp-54@mail.ru
Pobedinskiy Vladimir Viktorovich, doctor of technical sciences, professor, sub-department of service and technical operation, Ural State Forestry University (37 Siberian highway st., Ekaterinburg, Russia), pobed@e1.ru
Borodulin Igor' Viktorovich, applicant, Ural State Forestry University (37 Siberian highway st., Ekaterinburg, Russia), ugadn66@bk.ru
Chernitsyn Maksim Aleksandrovich postgraduate student, Ural State Forestry University (37 Siberian highway st., Ekaterinburg, Russia), skamer333@mail.ru
Kuz'minov Nikita Sergeevich, postgraduate student, Ural State Forestry University (37 Siberian highway st., Ekaterinburg, Russia), yaxik@e1.ru

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Background. The aim is to obtain the functional dependence of the loss of signal power at the radio frequency monitoring of the forest area through a network of RFID devices.
Materials and methods. The article describes the procedure of setting the content of fuzzy modeling problem, leading to ambiguities, the development of the rule base of fuzzy production. Synthesis of fuzzy model of the resulting dependence of falling signal power is made by means of Fuzzy Logic Toolbox MatLab applications.
Results. The resulting fuzzy output function is mathematically quite correct and can be used to predict the magnitude of the signal power loss at the various structural and parameters permittivity medium during RF monitoring.
Conclusions. The proposed function of the loss of signal power, built on the basis of fuzzy inference, takes into account the basic parameters of the forest environment, and comparing simulation results with experimental data points to adequately developed model avoided the exclusion to implement a new approach to solving the problem.

radio frequency monitoring of forest fund, drop in signal strength, parameters of the forest environment, fuzzy modeling, fuzzy inference.