| Autor: |
ZHAO Zhongchen, LIU Limin, XIE Hui, HAN Zhuangzhi, JING He |
| Jazyk: |
čínština |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Zhihui kongzhi yu fangzhen, Vol 46, Iss 4, Pp 124-133 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1673-3819 |
| DOI: |
10.3969/j.issn.1673-3819.2024.04.017 |
| Popis: |
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 proposed. Firstly, the amplitude, instantaneous frequency, instantaneous envelope of power spectrum and their composite features are taken as network input to compare their recognition accuracy in ResNet-CNN model. The composite features of amplitude 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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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