Abstract
Aiming at the difficult problem of the classification between flying bird and rotary-wing drone by radar, a micro-motion feature classification method is proposed in this paper. Using K-band frequency modulated continuous wave (FMCW) radar, data acquisition of five types of rotor drones (SJRC S70 W, DJI Mavic Air 2, DJI Inspire 2, hexacopter, and single-propeller fixed-wing drone) and flying birds is carried out under indoor and outdoor scenes. Then, the feature extraction and parameterization of the corresponding micro-Doppler (m-D) signal are performed using time-frequency (T-F) analysis. In order to increase the number of effective datasets and enhance m-D features, the data augmentation method is designed by setting the amplitude scope displayed in T-F graph and adopting feature fusion of the range-time (modulation periods) graph and T-F graph. A multi-scale convolutional neural network (CNN) is employed and modified, which can extract both the global and local information of the target’s m-D features and reduce the parameter calculation burden. Validation with the measured dataset of different targets using FMCW radar shows that the average correct classification accuracy of drones and flying birds for short and long range experiments of the proposed algorithm is 9.4% and 4.6% higher than the Alexnet- and VGG16-based CNN methods, respectively.
Highlights
The detailed flowchart of the classification method for flying birds and rotary-wing drones based on the data augmentation and the multi-scale convolutional neural network (CNN) is shown in Figure 20, which is consisted of four parts, radar echo processing, m-D dataset construction, CNN
The detailed flowchart of the classification method for flying birds and rotary-wing drones based on the data augmentation and the multi-scale CNN is shown in Figure 20 which is consisted of four parts, radar echo processing, m-D dataset construction, CNN
A feature extraction and classification method of flying birds and rotor drones is proposed based on data augmentation and a modified multi-scale CNN
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. A multi-scale CNN model is employed [25,26] and modified with other modules for learning and classification of micro-motion features of different types of targets, which can extract global and local information of m-D features. Description of the introduces the proposed data augmentation method; Section 3.3 the micro-motion characteristics of different types of drones and flying bird are analyzed based on the collected data; and Section 3.4 describes the composition and quantity of the dataset. Radar M-D Model of Flying Bird and Rotor Drone concludes the paper and presents future research directions
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