Auditory-Based Multi-Scale Amplitude-Aware Permutation Entropy as a Measure for Feature Extraction of Ship Radiated Noise

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).