Auditory-Based Multi-Scale Amplitude-Aware Permutation Entropy as a Measure for Feature Extraction of Ship Radiated Noise
Ping Wang, Mingsong Chen, Junyi Wang, Xiaofang Deng, Zhe Sage Chen · 2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC ) · 2022
Feature extraction of ship radiated noise (SRN) plays an important role for target detection and recognition. Aiming at extracting prominent and reliable features of SRN, auditory based multi-scale amplitude-aware permutation entropy (AMAAPE) is proposed in this paper. The proposed method consists of two parts that are signal decomposition and multi-scale entropy quantification. Firstly, a set of filters that can effectively simulate the mask effect and frequency response of human ears is designed, through which the SRN is decomposed into a series of band-limited signals. Then, multi-scale amplitude-aware permutation entropy (MAAPE) is employed to measure the complexity of each band-limited signal. Experimental results show that the proposed scheme achieves higher classification accuracy compared with multi-scale permutation entropy(MPE) and variational mode decomposition (VMD).