Multilevel Recognition of UAV-to-Ground Targets Based on Micro-Doppler Signatures and Transfer Learning of Deep Convolutional Neural Networks

Unmanned aerial vehicles (UAVs) are used in different scenarios. The need for intelligent and accurate recognition of targets using UAV-based radars is increasingly urgent. As unique information of targets, micro-Doppler characteristics have become an important basis for target recognition. For three typical ground targets including pedestrians, wheeled vehicles, and tracked vehicles, the micro-Doppler modulation of the UAV vibration on echo signals is analyzed first in this article. Second, the singular value decomposition (SVD) is adopted to suppress ground clutter and environmental noise. In order to decrease the computation and increase the contrast, all colored time–frequency diagrams of micro-Doppler signals are transferred into grayscale images. Third, a multilevel target recognition method based on the transfer learning of deep convolutional neural networks (DCNNs) is proposed. The first-level recognition is designed to accurately distinguish pedestrians, wheeled vehicles, and tracked vehicles, while the purpose of the second-level of recognition is to determine the three states of pedestrians including stepping, walking, and jogging. At last, experiments are carried out using a designed Doppler radar to prove the effectiveness of the proposed method. Results indicate that the method in this article not only avoids the complex feature extraction process, but also has higher accuracy and better robustness.