UAV Fast Signal Detection Algorithm with Convolutional Neural Network

As UAVs tend to be miniaturized and invisible, the successful recognition rate of traditional UAV identification methods such as time-frequency analysis, video surveillance, and radio interference is getting lower and lower. Aiming at this problem, Firstly, the traditional time-frequency analysis method is used to preprocess the data, obtain the time-frequency spectrum of the data, and construct the training set of convolutional neural network. Then build VGG network and residual network model based on maximum pooling, and use sample training set to train the sample model. Finally, the time-frequency spectrum of the obtained remote control signal is input to the learning model, and the classification and recognition results can be output.Finally, based on the Y550 software radio platform, three UAVs including parrot, DJ-m100 and DJ-a3 were measured. The experimental results show that when the learning rate is 0.1, the recognition rate of the method proposed in this paper can reach more than 97%. Under different learning rates, the recognition rate is still above 95%, which is greatly improved compared to the traditional time-frequency analysis and recognition method, and has a strong application prospect.