Efficient TFI-Based Depth-Tunable LPI Radar Waveform Recognition Network

Low probability of intercept (LPI) radar waveform recognition (LWR) based on deep learning can significantly enhance the recognition accuracy. However, most existing LWR networks overlook the algorithmic complexity and disparities between visual images and time-frequency images (TFIs). In practical applications, it is critical to enhance the recognition accuracy while minimizing the computational complexity to satisfy the reliability and timeliness requirements of an LWR network. To address this issue, we propose an efficient depth-tunable network (EDTN) for LWR. The EDTN reduces the computational complexity and enhances the recognition accuracy by adopting a flexible network structure, using convolution methods appropriate for TFIs, and combining several beneficial designs. Our experiments with a classical LPI radar waveform dataset demonstrate that the EDTN reduces the computational complexity by more than 95% over that of the state-of-the-art networks while maintaining the recognition accuracy.