Feature Extraction in Fractional Fourier Domain for Classification of Passive Sonar Signals

The acoustic signals radiated from the marine vessels contain information about their machinery characteristics that can be useful for the detection and classification purposes. To achieve reliable accuracy in classification task, informative discriminant features should be extracted from received signals. STFT (Short Time Fourier Transform) is the most basic and popular signal processing method in the classification process of passive sonar signals, but it suffers from some fatal shortages. In this work, we present an improved spectrogram based on the windowed fractional-Fourier transform of the acoustic signal with the optimal FrFT order, which accounts for signals with multiple non-stationary components. The discriminating capability of two groups of features extracted from the processed signal, using PCA and LDA techniques, have been compared. The achieved results declare the significant improvement in the classification accuracy by the new proposed method using the LDA feature extraction technique and the proper order of FrFT. However, in order to eliminate the constraints of searching for the proper FrFT-order and increasing the reliability and stability of practical sonar classification systems, a parallel combination of the proposed method is introduced.