Refined Composite Multi-Scale Reverse Weighted Permutation Entropy and Its Applications in Ship-Radiated Noise

Area of Science:

• Ocean acoustics
• Signal processing
• Complexity analysis

Background:

• Ship-radiated noise is crucial for understanding ship properties in complex ocean environments.
• Permutation entropy (PE) is efficient for ship-radiated noise complexity but lacks amplitude information and multi-scale analysis.
• Existing methods struggle with amplitude and scale limitations in characterizing ship noise.

Purpose of the Study:

• To develop an advanced method for analyzing ship-radiated noise complexity.
• To address the limitations of traditional permutation entropy (PE) in amplitude information and single-scale analysis.
• To propose a novel feature extraction and classification scheme for ship-radiated noise.

Main Methods:

• Introduced refined composite multi-scale reverse weighted permutation entropy (RCMRWPE).
• Utilized refined composite multi-scale processing (RCMP) for improved coarse-graining stability and multi-scale analysis.
• Employed reverse weighted PE (RWPE) for enhanced inter-class separability and noise robustness.
• Developed a feature extraction scheme based on RCMRWPE combined with discriminant analysis classifier (DAC).

Main Results:

• RCMRWPE effectively captures both amplitude and multi-scale characteristics of ship-radiated noise.
• The proposed feature extraction scheme demonstrated superior distinguishing ability and stability compared to MPE, MWPE, and MRPE.
• The RCMRWPE-based classification method achieved the highest recognition rate in comparative experiments.

Conclusions:

• RCMRWPE offers a more comprehensive and robust approach to characterizing ship-radiated noise complexity.
• The proposed feature extraction and classification method significantly enhances ship identification accuracy.
• This research provides an effective tool for analyzing complex acoustic signals in marine environments.