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

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.