Recognition of LPI radar signal based on dual efficient network

作     者：Li Hui Qin Yibo Hou Qinghua Cheng Yuanyang 

作者机构：School of Physics and Electronic Information Engineering， Henan Polytechnic University 

出 版 物：《The Journal of China Universities of Posts and Telecommunications》 (中国邮电高校学报(英文))

年 卷 期：2024年

核心收录：

学科分类：080904[工学-电磁场与微波技术] 0810[工学-信息与通信工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 081105[工学-导航、制导与控制] 081001[工学-通信与信息系统] 081002[工学-信号与信息处理] 0825[工学-航空宇航科学与技术] 0811[工学-控制科学与工程] 

摘      要：Addressing the issue of low pulse identification rates for low probability of intercept (LPI) radar signals under low signal-to-noise ratio (SNR) conditions， this paper aims to investigate a new method in the field of deep learning to efficiently recognize modulation types of LPI radar signals. A novel algorithm combining dual efficient network (DEN) and non-local means (NLM) denoising is proposed for the identification and selection of LPI radar signals. We simulate time-domain signals for 12 radar modulation types， adding Gaussian white noise at various SNRs to replicate complex electronic countermeasure scenarios. On this basis， the noisy radar signals undergo Choi-williams distribution (CWD) time-frequency transformation， converting the signals into two-dimensional time-frequency images (TFIs). The TFIs are then denoised using the NLM algorithm. Finally， the denoised data is fed into the designed DEN for training and testing， with the selection results output through a Softmax classifier. Simulation results demonstrate that at an SNR of -8dB， the algorithm can achieve a recognition accuracy of 97.22% for LPI radar signals， exhibiting excellent performance under low SNR conditions. Comparative demonstrations prove that the DEN has good robustness and generalization performance under conditions of small sample sizes. This research provides a novel and effective solution for further improving the accuracy of identification and selection of LPI radar signals.