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Content Provider | IET Digital Library |
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Author | Wang, Cai Li, Yan Gao, Meiguo |
Abstract | In this study, a target classification method is proposed based on a third-order cyclic statistics technique. The authors introduce cyclic bispectrum (CBS) to reveal the non-linear cyclic nature contained by the micro-Doppler signal, and it is observed that the non-zero peaks generated by some cyclic non-linear nature form unique distribution patterns on CBS slices for different targets. Then, a Renyi entropy is calculated for each CBS slice to measure the information content and thus achieve an entropy sequence. Subsequently, considering the entropy sequence as a feature vector, the support vector machine classifier is used to perform the target classification. Experimental results based on real measured data validate the effectiveness of the method. |
Starting Page | 7717 |
Ending Page | 7720 |
Page Count | 4 |
Volume Number | 2019 |
e-ISSN | 20513305 |
Issue Number | Issue 21, Nov (2019) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/joe/2019/21 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0672 |
Journal | The Journal of Engineering |
Publisher | The Institution of Engineering and Technology |
Publisher Date | 2019-08-13 |
Access Restriction | Open |
Rights License | Creative Commons Attribution-Non Commercial-No Derivs License (http://creativecommons.org/licenses/by-nc-nd/3.0/) |
Subject Keyword | CBS Slice Cyclic Bispectrum Cyclic Nonlinear Nature Form Unique Distribution Pattern Data Handling Technique Digital Signal Processing Entropy Entropy Sequence Feature Extraction Feature Vector Information Theory Knowledge Engineering Technique MicroDoppler Signal Nonzero Peaks Pattern Classification Renyi Entropy Feature Signal Classification Signal Processing And Detection Statistical Analysis Statistics Support Vector Machine Support Vector Machine Classifier Target Classification Method Third-order Cyclic Statistics Technique |
Content Type | Text |
Resource Type | Article |
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