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Content Provider | IET Digital Library |
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Author | Hao, Zhimei Yu, Wen Chen, Wei |
Abstract | Dense false target jamming can affect radar detection performance severely. A method of dense false targets jamming recognition based on time-frequency atomic decomposition theory and support vector machine (SVM) is proposed to solve the difficulty of dense false targets jamming identification. According to the feature of ambiguity function of dense false targets jamming signal, a Gabor sub-dictionary which has adaptive variation with signal is designed. The signal is expanded into the corresponding Gabor time-frequency dictionary by sparse decomposition. After Gabor atomic time-frequency parameters are extracted as individual feature vectors, SVM is utilised for classification and recognition. The experimental results show that the extracted Gabor atomic time-frequency parameters can effectively represent the essential features of target and dense false targets jamming, respectively, and this method has a high recognition rate. |
Starting Page | 6354 |
Ending Page | 6358 |
Page Count | 5 |
Volume Number | 2019 |
e-ISSN | 20513305 |
Issue Number | Issue 20, Oct (2019) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/joe/2019/20 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0147 |
Journal | The Journal of Engineering |
Publisher | The Institution of Engineering and Technology |
Publisher Date | 2019-05-30 |
Access Restriction | Open |
Rights License | Creative Commons Attribution -Non Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) |
Subject Keyword | Algebra Decomposition Dense False Target Jamming Recognition Method Digital Signal Processing Electrical Engineering Computing Electromagnetic Compatibility Gabor Atomic Time-frequency Parameter Extraction Gabor Time-frequency Dictionary Interference Jamming Knowledge Engineering Technique Mathematical Analysis Radar Computing Radar Detection Radar Detection Performance Radar Equipment Signal Classification Signal Detection Sparse Decomposition Support Vector Machine SVM System And Application Time-frequency Analysis Time-frequency Atomic Decomposition Theory Vector |
Content Type | Text |
Resource Type | Article |
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