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unmanned aerial vehicle (UAV), today, plays prominent role in many parts of human activity. Not only in military area, also UAV using in emergency response, urban planning, healthcare, agriculture, waste management, mining, telecommunication, etc. One of serious problems exploitation UAV is accuracy of positioning. There're many sources of signal's fluctuation: satellite clocks, satellite orbits, ionosphere and troposphere, multipath, etc. But ionosphere creates largest fluctuation of signal. These fluctuations are characterizing by one of distribution laws: Nakagami, normal distribution law, Rayleigh and Rice. One of efficient methods to solve this problem is applying of adapting control system, based on Flight Control Center. To ensure the object status identification it is reasonable to use methods of object recognition, including machine learning methods. At working this system there exist probability of making type I and type II errors during salvation of identification problems. This work proposes an approach to implementing the threshold optimization for classifying the states of satellite communication systems, when disorting of signals described by normal distribution law. This research is supported by the scientific project «Development of a multi-rotor robotic unmanned aerial vehicle using a strapdown inertial navigation system» in the Federal Target Program 2014-2020 (unique identifier RFMEFI57818X0222) with the financial support of the Ministry of Science and Higher Education of Russian Federation. Keywords—GPS, GLONASS, UAV, type I errors, type II errors, normal distribution law.

Optimizing Classification Thresholds of Status of Transionospheric Communication Channel for Decreased Quadrocopter's Positioning Errors