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Electronics 2018, 7(5), 59; https://doi.org/10.3390/electronics7050059

Radar Waveform Recognition Based on Time-Frequency Analysis and Artificial Bee Colony-Support Vector Machine

College of Information and Telecommunication, Harbin Engineering University, Harbin 150001, China
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Received: 28 March 2018 / Revised: 20 April 2018 / Accepted: 24 April 2018 / Published: 27 April 2018
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Abstract

In this paper, a system for identifying eight kinds of radar waveforms is explored. The waveforms are the binary phase shift keying (BPSK), Costas codes, linear frequency modulation (LFM) and polyphase codes (including P1, P2, P3, P4 and Frank codes). The features of power spectral density (PSD), moments and cumulants, instantaneous properties and time-frequency analysis are extracted from the waveforms and three new features are proposed. The classifier is support vector machine (SVM), which is optimized by artificial bee colony (ABC) algorithm. The system shows well robustness, excellent computational complexity and high recognition rate under low signal-to-noise ratio (SNR) situation. The simulation results indicate that the overall recognition rate is 92% when SNR is −4 dB. View Full-Text
Keywords: radar waveform recognition; electronic warfare; support vector machine; artificial bee colony radar waveform recognition; electronic warfare; support vector machine; artificial bee colony
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Liu, L.; Wang, S.; Zhao, Z. Radar Waveform Recognition Based on Time-Frequency Analysis and Artificial Bee Colony-Support Vector Machine. Electronics 2018, 7, 59.

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