Robust and fast algorithm for extracting the periodic feature from jet engine modulation signals

• Published in: Journal of engineering (Stevenage, England) Vol. 2019; no. 20; pp. 6507 - 6511
• Main Authors: Sun, Jingming, Yu, Junpeng
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 01.10.2019; Wiley
• Subjects: accurate extraction; aircraft target recognition; chopping frequency; fast algorithm; feature extraction; IET International Radar Conference (IRC 2018); image processing; JEM signal; jet engine modulation signals; jet engines; logical algorithms; low-pass filters; measured JEM signals; modulation phenomenon; periodic feature; radar returns; radar target recognition; representative feature; robust algorithm; rotating structure; significant periodic characteristics; stable extraction; wavelet transforms; Wigner distribution; image processing; aircraft target recognition; logical algorithms; radar returns; wavelet transforms; representative feature; modulation phenomenon; robust algorithm; periodic feature; jet engines; rotating structure; feature extraction; JEM signal; radar target recognition; significant periodic characteristics; measured JEM signals; accurate extraction; low-pass filters; fast algorithm; stable extraction; Wigner distribution; chopping frequency; jet engine modulation signals
• ISSN: 2051-3305; 2051-3305
• DOI: 10.1049/joe.2019.0351

Jet engine modulation (JEM), a modulation phenomenon induced by the rotating structure of jet engines, is a representative feature extracted from the radar returns for aircraft target recognition. As one of the most significant periodic characteristics of JEM, the chopping frequency plays an important role in aircraft target recognition. However, existing methods either need complicated logical algorithms to get an accurate estimation of the chopping frequency or exploit the chopping frequency as priori information for further feature extraction, and neither of these two scenarios is practical. Here, the authors propose a robust and fast algorithm for extracting the chopping frequency, which uses wavelet decomposition combined with autocorrelation to process the analytic form of the JEM signal. Application results of measured JEM signals demonstrate that the proposed algorithm is effective and practical for stable and accurate extraction of the periodic feature from JEM signals.