Noise-Resistant Feature Extraction from Measured Data of a Passive Sonar

Vahid Bagheri, Vahid Izadi, Kamran Davoodi


In this paper, two different methods for the classification of passive sonar data based on time-frequency methods are studied. In the first step, two passive sonar signal classifier systems are implemented using the Short-Time Fourier Transformation (STFT) approach and Short-Time Fractional Fourier Transformation (STFrFT). The performance of the proposed classifier for passive sonars in the presence of an increased amount of noise is investigated in this study. The results showed that the classification system based on STFT has better efficiency in classifying the original signals. The method based on STFT showed more resistance to noisy signal classification so that the accuracy of the classification system was reduced by a smoother slope than the STFT classification system. The loss of accuracy in the STFrFT-based method for increasing the noise level is -0.15, while for STFT-based method is equal to -0.37.


Feature extraction, Short-Time Fourier Transformation (STFT), Short-Time Fractional Fourier Transformation (STFrFT), Sonar Data ClassificationBagheri, V., Jahromi, M. S., Keshavarz, A., & Rostami, H. (2014). Time-Frequency Signal Processing Based on Fract

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