Recognition and separation of radar aliasing signals based on CNN and SVDD

Yao Qin; Hui Liu; Yuanfu Guo; Xianzhen Chen

doi:10.1117/12.2616225

15 February 2022 Recognition and separation of radar aliasing signals based on CNN and SVDD

Yao Qin, Hui Liu, Yuanfu Guo, Xianzhen Chen

Proceedings Volume 12166, Seventh Asia Pacific Conference on Optics Manufacture and 2021 International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2021); 1216637 (2022) https://doi.org/10.1117/12.2616225
Event: Seventh Asia Pacific Conference on Optics Manufacture and 2021 International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2021), 2021, Hong Kong, Hong Kong

Abstract

The identification and separation of aliasing signals play a key role in radar electronic countermeasures. Due to the complex and diverse waveforms of radar radiator signals and the characteristics of rapid parameter changes, as well as the uncertainty of the aliasing types of unknown signals and the uncertainty of signal composition, the identification and separation of radar aliasing signals are facing challenges. In response to the above challenges, a recognition architecture based on Convolutional Neural Network (CNN) and Support Vector Data Description (SVDD) was designed to identify and separate aliasing signals of unknown signal types. First, the CNN network extracts the features of the single-signal image and submits it to the SVDD classifier for training, so that the aliasing signal can be filtered from the unknown signal type (including the aliasing signal and the single signal). Secondly, we put the filtered aliasing signals into the CNN network for identification and classification to obtain the signal composition and aliasing type. Finally, according to the identified signal aliasing type and signal type, combined with MATLAB, different methods are selected to separate the aliasing signal. Simulation experiments prove that the recognition accuracy of this method is higher than that of traditional methods.

Citation Download Citation

Yao Qin, Hui Liu, Yuanfu Guo, and Xianzhen Chen "Recognition and separation of radar aliasing signals based on CNN and SVDD", Proc. SPIE 12166, Seventh Asia Pacific Conference on Optics Manufacture and 2021 International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2021), 1216637 (15 February 2022); https://doi.org/10.1117/12.2616225

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KEYWORDS

Signal processing

Time-frequency analysis

Radar

Signal to noise ratio

Electronic filtering

Modulation

MATLAB

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