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随着现代电子战的飞速发展，基于数字射频存储器转发的新型干扰层出不穷，如何快速有效地识别这类干扰成为现今研究的热点问题。针对于此，提出了一种基于改进MobileV3Net的脉冲雷达干扰识别研究，使用MobileV3Net作为基本网络框架，添加了动态卷积模块和高效通道注意力模块，实现了自动提取特征的小样本下8类干扰的有效识别。仿真结果表明，该网络的训练时间大大减少，且在轻量训练样本下依然能保持95%以上的识别准确率，在-10～10 dB下，平均识别率在99%以上，证明该方法具有更强的鲁棒性，更高的准确度，更好的轻量性。
Abstract：With the rapid development of modern electronic warfare, new types of jamming based on digital radio frequency memory forwarding are emerging in an endless stream.How to quickly and effectively identify this kind of jamming has become a hot issue in current research.In view of this, this paper proposes a research into the recognition of pulse radar jamming based on improved MobileV3Net.In the research, MobileV3Net is used as the basic network framework, and a dynamic convolution module and an efficient channel attention module are added, then the effective recognition of eight types of jamming under small samples with automatic extraction features is realized.The simulation results show that the training time of the network is greatly reduced, the recognition accuracy can still maintain more than 95% under lightweight training samples, and the average recognition rate is more than 99% in-10～10 dB,which proves that the method has more robustness, higher accuracy and better lightweight.
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基本信息:
DOI：10.16426/j.cnki.jcdzdk.2024.04.001
中图分类号:TN974
引用信息:
[1]郭立民,鄂璟仪,黄文青.基于改进MobileV3Net的脉冲雷达干扰识别方法[J].舰船电子对抗,2024,47(04):1-7.DOI:10.16426/j.cnki.jcdzdk.2024.04.001.
基金信息:
国家自然科学基金,项目编号：62071137