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一种ER-C-L网络模型下的有源干扰识别算法

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针对强噪声环境下雷达有源干扰识别准确率不高的问题,提出了一种基于一维复合特征的 ER-C-L(Extended ResNet-CNN-LSTM)网络模型算法.首先将幅度、瞬时频率和功率谱瞬时包络及其复合特征作为网络输入,比较其在ResNet-CNN模型上的识别准确率,选取检测概率高且数据量小的幅度与功率谱瞬时包络复合特征为最优特征.然后将该复合特征输入ER-C-L网络对六种新型有源干扰进行识别,仿真结果表明,在干噪比(Jamming Noise Ratio,JNR)-10 dB的强噪声环境下,识别准确率为 98.5%,与CNN、ResNet-CNN、扩展ResNet-CNN和LSTM等其他深度学习算法相比,具有更高的干扰识别准确率.
An active jamming recognition algorithm based on ER-C-L network model
To solve the problem of low recognition accuracy of radar active jamming in strong noise environment,an algorithm for ER-C-L(Extended ResNet-CNN-LSTM)network model based on one-dimensional composite features is pro-posed.Firstly,the amplitude,instantaneous frequency,instantaneous envelope of power spectrum and their composite fea-tures are taken as network input to compare their recognition accuracy in ResNet-CNN model.The composite features of am-plitude and instantaneous envelope of power spectrum with high detection probability and small data volume are selected as the optimal features.Then,the complex features are injammed into the ER-C-L network to identify six new active jamming models.Simulation experiments show that the recognition accuracy of jamming is 98.5%in strong noise environment within the JNR of-10 dB.Compared with other deep learning algorithms such as CNN,ResNet-CNN,extended ResNet-CNN and LSTM,it has higher interference recognition accuracy.

active jamming recognitionconvolutional neural networkscompound feature

赵忠臣、刘利民、解辉、韩壮志、荆贺

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陆军工程大学石家庄校区,河北 石家庄 050003

有源干扰识别 卷积神经网络 复合特征

2024

指挥控制与仿真
中国船舶重工集团公司 第七一六研究所

指挥控制与仿真

CSTPCD
影响因子:0.309
ISSN:1673-3819
年,卷(期):2024.46(4)
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