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

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Polyphase system Support vector machine Phase-shift keying Waveform Radar Keying Computer science Artificial bee colony algorithm Frequency-shift keying Speech recognition Artificial intelligence Pattern recognition (psychology) Electronic engineering Algorithm Engineering Telecommunications Bit error rate Decoding methods Channel (broadcasting) Demodulation

Lutao Liu , Shuang Wang , Zhongkai Zhao ·

YOU? · · 2018 · Open Access · · DOI: https://doi.org/10.3390/electronics7050059 · OA: W2799693395

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.