Aircraft classification method based on the kurtosis–skewness feature and wavelet decomposition and linear discriminant analysis

• Published in: Journal of engineering (Stevenage, England) England), 2019-11, Vol.2019 (21), p.7855-7859
• Main Authors: Kang, Pengpeng, Chen, Zhiming
• Format: Article
• Language: English
• Subjects: aircraft classification method; aircraft target echoes; extraction methods; feature extraction; IET International Radar Conference (IRC 2018); image classification; inter-spectral interval; jet engine modulation feature; jet engines; kurtosis; linear discriminant analysis; modulation; modulation line spectrum; modulation wave period; noise; normalised amplitude distribution; observation time; pattern classification; PRF; signal-to-noise ratio; skewness feature; spectral analysis; spectral estimation methods; statistical analysis; strong anti-noise capability; wavelet decomposition; wavelet transforms
• ISSN: 2051-3305; 2051-3305
• DOI: 10.1049/joe.2019.0679

At present, most of the jet engine modulation feature extraction methods are based on the modulation wave period or the inter-spectral interval of the modulation line spectrum. However, such spectral estimation methods are often difficult to obtain good classification performance due to the signal-to-noise ratio, pulse repetition frequency (PRF) and observation time. The statistical analysis of the three types of aircraft target echoes shows that there is a significant difference in the normalised amplitude distribution, and based on this, the kurtosis–skewness feature is extracted to classify the targets. This feature has a strong anti-noise capability, the requirement for PRF and observation time is not high, and one of the parameters can be used to make up for another parameter, so we can make a balance between PRF and time if needed. The simulation test proves that the proposed method has good classification performance.