WTCESNet: A Novel Low Slow Small Target Classification Network Model

With the rapid development of UAV technology, the illegal use of UAVs poses a serious threat to public safety, and the feature extraction of UAV echo signals is crucial for target identification, however the micro-Doppler feature extraction based on convolutional neural network has the problem of insufficient feature extraction, which has a greater impact on the accurate identification of UAV targets . In this paper, a new UAV recognition network model is proposed , named Wavelet Collaborative and Effective Channel Spatial Attention Network (WTCESNet),which takes RepViT as the backbone network, and designs a collaborative and effective channel spatial attention module (CES),which is capable of fine-tuning the micro-Doppler features of the echo signals through the synergistic effect of effective channel attention and spatial attention. At the same time, wavelet convolution is introduced to expand the sensory field of the model feature extraction, which effectively improves the network’s recognition ability. Finally, a hybrid loss function is designed, which consists of a focus loss and a label smoothing loss function . Avoid the error due to sample imbalance. The experimental results show that the proposed method achieves accuracy rates of 95.98 % (15 dB) , 93.76% (10 dB) , and 86.11% (-10 dB) under different signal-to-noise ratios compared with Vit, Swin Transformer, and other methods.