An anti‐jamming method in multistatic radar system based on convolutional neural network
Abstract For the existing jamming discrimination methods on the multistatic radar system, the single feature of target echo space correlation is utilised as the metric, which leads to the lack of comprehensive feature extraction and universal discrimination algorithm. In this study, a discrimination...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi-IET
2022-04-01
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| Series: | IET Signal Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/sil2.12085 |
| _version_ | 1797428179597524992 |
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| author | Jieyi Liu Maoguo Gong Mingyang Zhang Hao Li Shanshan Zhao |
| author_facet | Jieyi Liu Maoguo Gong Mingyang Zhang Hao Li Shanshan Zhao |
| author_sort | Jieyi Liu |
| collection | DOAJ |
| description | Abstract For the existing jamming discrimination methods on the multistatic radar system, the single feature of target echo space correlation is utilised as the metric, which leads to the lack of comprehensive feature extraction and universal discrimination algorithm. In this study, a discrimination method in a multistatic radar system based on the convolutional neural network is proposed. This proposal combines the advantages of multiple‐radar systems cooperative detection technology with the convolutional neural network, and effectively applies to the field of anti‐deception jamming, which takes full advantage of unknown information of echo data to obtain multi‐dimensional, comprehensive, complete and deep feature differences besides correlation, so as to achieve a better jamming discrimination effect. The simulation results show that the proposed method can extract the multidimensional and separable essential features of echoes, and all these features have a strong degree of differentiation between targets and jamming, which effectively reduce the influence of noise and pulse number. At the same time, the influence of radar distribution on jamming discrimination under non‐ideal conditions is relieved, when the correlation coefficient of the true target reaches 0.4, the discrimination probability remains above 85%, which broadens the boundary conditions of the application process. |
| first_indexed | 2024-03-09T08:55:18Z |
| format | Article |
| id | doaj.art-8d2a428238d644909dca25f2e95c352c |
| institution | Directory Open Access Journal |
| issn | 1751-9675 1751-9683 |
| language | English |
| last_indexed | 2024-03-09T08:55:18Z |
| publishDate | 2022-04-01 |
| publisher | Hindawi-IET |
| record_format | Article |
| series | IET Signal Processing |
| spelling | doaj.art-8d2a428238d644909dca25f2e95c352c2023-12-02T13:23:18ZengHindawi-IETIET Signal Processing1751-96751751-96832022-04-0116222023110.1049/sil2.12085An anti‐jamming method in multistatic radar system based on convolutional neural networkJieyi Liu0Maoguo Gong1Mingyang Zhang2Hao Li3Shanshan Zhao4School of Electronic Engineering Xidian University Xi'an ChinaSchool of Electronic Engineering Xidian University Xi'an ChinaSchool of Electronic Engineering Xidian University Xi'an ChinaSchool of Electronic Engineering Xidian University Xi'an ChinaCollege of Electronic and Optical Engineering Nanjing University of Posts and Telecommunications Nanjing ChinaAbstract For the existing jamming discrimination methods on the multistatic radar system, the single feature of target echo space correlation is utilised as the metric, which leads to the lack of comprehensive feature extraction and universal discrimination algorithm. In this study, a discrimination method in a multistatic radar system based on the convolutional neural network is proposed. This proposal combines the advantages of multiple‐radar systems cooperative detection technology with the convolutional neural network, and effectively applies to the field of anti‐deception jamming, which takes full advantage of unknown information of echo data to obtain multi‐dimensional, comprehensive, complete and deep feature differences besides correlation, so as to achieve a better jamming discrimination effect. The simulation results show that the proposed method can extract the multidimensional and separable essential features of echoes, and all these features have a strong degree of differentiation between targets and jamming, which effectively reduce the influence of noise and pulse number. At the same time, the influence of radar distribution on jamming discrimination under non‐ideal conditions is relieved, when the correlation coefficient of the true target reaches 0.4, the discrimination probability remains above 85%, which broadens the boundary conditions of the application process.https://doi.org/10.1049/sil2.12085convolutional neural networkjamming discriminationfeature extractionmultistatic radar system |
| spellingShingle | Jieyi Liu Maoguo Gong Mingyang Zhang Hao Li Shanshan Zhao An anti‐jamming method in multistatic radar system based on convolutional neural network IET Signal Processing convolutional neural network jamming discrimination feature extraction multistatic radar system |
| title | An anti‐jamming method in multistatic radar system based on convolutional neural network |
| title_full | An anti‐jamming method in multistatic radar system based on convolutional neural network |
| title_fullStr | An anti‐jamming method in multistatic radar system based on convolutional neural network |
| title_full_unstemmed | An anti‐jamming method in multistatic radar system based on convolutional neural network |
| title_short | An anti‐jamming method in multistatic radar system based on convolutional neural network |
| title_sort | anti jamming method in multistatic radar system based on convolutional neural network |
| topic | convolutional neural network jamming discrimination feature extraction multistatic radar system |
| url | https://doi.org/10.1049/sil2.12085 |
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