Method of Determining Parameters of Modulation of Uav Signals Using Artificial Neural Networks

Abstract

The article is devoted to the consideration of the issue of determining the parameters of the modulation of signals of unmanned aerial vehicles using artificial neural networks by recognizing the types of digital modulation and is performed by a system that automatically classifies the type of digital modulation of the received signal. Recognition of digital modulation types is used, which automatically classifies the type of digital modulation of the received signal. The following issues are covered in the article: the analysis of existing approaches in the task of automatic recognition of types of digital modulation is carried out; the analysis and classification of informative features in the task of automatic recognition is carried out the following types of digital modulation: 2-PSK, 4-PSK, 8-PSK, 2-FSK, 8-QAM, 16-QAM, 64-QAM and OFDM. This article uses a neural network approach based on cumulative characteristics. To solve these problems, the methods of calculation and selection of informative cumulative features described in the developed model of the system of automatic recognition of digital modulation types by means of a neural network on cumulative features at a known value of carrier frequency are described. A model of the system of automatic recognition of digital modulation types at a known value is constructed carrier frequency. An algorithm for automatic recognition of digital modulation types has been developed using a multilayer neural network. The influence of noise in the communication channel on the probability of recognizing the types of digital modulation at a known value of the carrier frequency is investigated. It was found that regardless of the type of noise in the communication channel, the law of error distribution in IQ data becomes close to normal. This fact is one important argument for the use of cumulative features in the task of automatic recognition of types of digital modulation. Therefore, the task of automatic recognition of digital modulation types is quite relevant. Further research may be aimed at expanding the range of high-order cumulative features used, due to which it is possible to increase the probability of correct recognition of types of digital modulation, and solving the recognition problem at an unknown value of the frequency and initial phase of the carrier signal.