Feature extraction of underwater acoustic signal based on variational mode decomposition and fractional-order chaotic oscillator

Underwater acoustic signal (UAS) feature extraction plays a crucial role in marine target recognition. As human activities and research in marine environments continue to increase, the task of extracting meaningful features from UAS has become more challenging. To address this issue, this paper proposed an enhanced feature extraction method that improves the accuracy of marine target recognition. The method combines an improved variational mode decomposition, fractional order Duffing (FOD) oscillator, improved multiscale amplitude-aware permutation entropy (IMAAPE), and an improved least squares support vector machine by turbulent flow of water-based optimization (TFWO-LSSVM). By decomposing UAS into intrinsic mode functions (IMFs), the method selects the IMF with the smallest IMAAPE value for feature extraction. The FOD oscillator is then used to detect the line spectrum of the selected IMF and determine the frequency range. The frequency corresponding to the smallest IMAAPE value is identified as the line spectrum frequency. Finally, the extracted features are input into the TFWO-LSSVM for recognition, achieving a recognition rate of 97.4%. This method demonstrates high accuracy, and the success rate of actual marine biological signal extraction reaches 95.1%. The proposed method offers significant advancements in marine target recognition, with potential applications in environmental monitoring and marine biology research.