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Content Provider | IET Digital Library |
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Author | Cao, Yang Bai, Jin Liang Li, Hongbo Zhao, Jian Liu, Shuo Gao, Zhiyong |
Abstract | Signal representation is to transform the intercepted original signal into feature parameters, which is the basis for the electronic reconnaissance system to analyse and identify a radar. The traditional method is to manually design a limited number of features with prior knowledge, which can only achieve the desired results in specific applications. Therefore, it is necessary to study signal representation methods that are more adaptable, reasonable, and effective. This study proposes a feature-level design scheme that extracts multi-level signal feature parameters to obtain deep essential feature representations, which is consistent with deep-learning frameworks. Through the decomposition through wavelet packet transform (WPT), the best sub-band tree structure of the signal can be obtained, and the depth representation of the signal can be well achieved, which can meet requirements of time–frequency analysis automatically. Here, Symlets wavelet packet decomposition (WPD) is used as an example to implement multi-layer signal feature representation. The combination of characteristic parameters at different levels can reflect the different information contained in different radar signals. The simulation results show that the method can effectively identify the modulation mode of radar signals and enhance the information mining ability from radar signals. |
Starting Page | 6282 |
Ending Page | 6286 |
Page Count | 5 |
Volume Number | 2019 |
e-ISSN | 20513305 |
Issue Number | Issue 19, Oct (2019) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/joe/2019/19 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0256 |
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 License (http://creativecommons.org/licenses/by/3.0/) |
Subject Keyword | Characteristic Parameter Computer Vision And Image Processing Technique Deep Essential Feature Representations Deep Representation Method Deep-learning Frameworks Depth Representation Desired Result Different Radar Signal Digital Signal Processing Electronic Reconnaissance System Feature Extraction Feature-level Design Scheme Filtering Method in Signal Processing Filtering Theory Image Representation Integral Transforms Intercepted Original Signal Knowledge Engineering Technique Learning in AI Multilayer Signal Feature Representation Multilevel Signal Feature Parameter Prior Knowledge Radar Emitter Signal Radar Signal Processing Signal Classification Signal Processing And Detection Signal Representation Statistics Study Signal Representation Method Symlets Wavelet Packet Time-frequency Analysis Wavelet Packet Decomposition Wavelet Transform |
Content Type | Text |
Resource Type | Article |
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