Jamming Recognition of Carrier-Free UWB Cognitive Radar Based on MANet

The satisfaction of various basic requirements of cognitive radar by ultra-wideband (UWB) signals makes UWB cognitive radar attract extensive attention. The variety and large dynamic range of jamming in the UWB spectrum range make jamming identification critical and challenging. However, the traditional method has low recognition accuracy, high computational complexity, and difficulty in multisignal recognition. In this article, we propose a multiscale attention network (MANet) for carrier-free UWB cognitive radar to identify target signals and nine types of jamming signals. MANet extracts different fine features by multiscale dilation convolution. The features are stitched together in the channel dimension. The subtle features that are beneficial for recognition are then substantially enhanced using channel attention blocks. The proposed method combines the time- and frequency-domain features to improve the recognition performance by using the powerful feature extraction ability and generalization ability of MANet. Simulation results show that the overall recognition accuracy of the method is 93.1%, with less storage space, shorter floating-point operations (FLOPs), and inference time than the five recognition methods, and better and more stable recognition performance is also achieved at low jamming-to-noise ratios (JNRs).