Automatic feature extraction from jet engine modulation signals based on an image processing method

This study presents an automatic method for extracting the jet engine features from the joint time-frequency (JTF) representation of jet engine modulation (JEM) signals. First, empirical mode decomposition with adaptive low-pass filtering was employed to extract the first harmonic component of the JEM signal. Then, a smoothed pseudo Wigner–Ville distribution (SPWVD) technique was used for acquiring the refined JTF representation. After converting the SPWVD result into an image with RGB colours, the green component was extracted as a representative of the JEM component. Finally, the peaks detected from the extracted green component can represent the jet engine features. The approach proposed in this study is significant because the overall procedures for extracting the jet engine features are not manual but automatically performed based on the image processing method. Application to measured JEM signals demonstrated that the automatic feature presented in this study improved the accuracy of JEM analysis and is expected to be efficient for real-time radar non-cooperative target recognition.