UAV recognition method based on feature extraction

This paper first selects some features with classification effect according to the flight paths of drones and birds. In terms of echo intensity and movement, it selects features such as zero crossing number and dispersion coefficient, and then uses each feature for separate classification to compare which feature has better classification effect. Then, these features are combined for processing, and CNN network is used for classification, and it is verified that these features have a good classification effect. Finally, SVM, KNN and CNN-T are also used to identify the UAV, and the results of the four classification methods are compared. When CNN-T model is used, the recognition rate of the UAV is the best, reaching 0.91.