Recognition of LPI radar signal based on dual efficient network

doi:10.19682/j.cnki.1005-8885.2024.0022

中国邮电高校学报(英文) ›› 2024, Vol. 31 ›› Issue (5): 12-22.doi: 10.19682/j.cnki.1005-8885.2024.0022

Recognition of LPI radar signal based on dual efficient network

李辉¹,秦怡博²,侯庆华²,程远洋³

1. 河南理工大学电气工程与自动化学院
2. 河南理工大学
3. 河南理工大学物理与电子信息学院

收稿日期:2023-10-23 修回日期:2024-03-27 出版日期:2024-10-31 发布日期:2024-10-31
通讯作者: 秦怡博 E-mail:qinyibo123@139.com

Recognition of LPI radar signal based on dual efficient network

Received:2023-10-23 Revised:2024-03-27 Online:2024-10-31 Published:2024-10-31

摘要/Abstract

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 recognize modulation types of LPI radar signals efficiently. A novel algorithm combining dual efficient network ( DEN) and non-local means ( NLM) denoising was proposed for the identification and selection of LPI radar signals. Time-domain signals for 12 radar modulation types were simulated, 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 (2D) 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 - 8 dB, 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.

关键词:

Choi-Williams distribution ( CWD), dual efficient network ( DEN), low probability of intercept ( LPI) radar signals, non-local means ( NLM) denoising

Abstract:

Key words:

Choi-Williams distribution ( CWD), dual efficient network ( DEN), low probability of intercept ( LPI) radar signals, non-local means ( NLM) denoising

中图分类号:

TN974

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Recognition of LPI radar signal based on dual efficient network

Recognition of LPI radar signal based on dual efficient network

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