A novel feature extraction method for ship-radiated noise based on hierarchical refined composite multi-scale dispersion entropy-based Lempel-Ziv complexity

Accurate recognition of ship-radiated noise (SRN) is particularly important for coastal defense, where feature extraction is the key technology. As a recently proposed nonlinear dynamics index, dispersion entropy-based Lempel-Ziv complexity (DELZC) can effectively quantify the information contained in SRN. However, DELZC characterizes inadequately the complexity feature of SRN with the ignorance of the information under different scales and information hidden in high frequency. To correct these drawbacks, the refined composite coarse-graining operation is integrated into DELZC, and the refined composite multi-scale DELZC (RCMDELZC) is put forward and adequately reflects the scale information, subsequently the technology of hierarchical decomposition is introduced and proposed the hierarchical RCMDELZC (HRCMDELZC), which takes into account the abundant feature information of the signal contained in the time scale and different frequency bands. Then, on the basis of HRCMDELZC, a feature extraction method for SRN by combining with maximum relevance minimum redundancy (mRMR) is proposed. The simulation results show that HRCMDELZC can effectively classify different noise and chaotic signals; the realistic experimental results deliver that the HRCMDELZC features of SRNs have more excellent separability compared with the other seven categories of complexity features, and the proposed feature extraction method has the highest classification accuracy for various SRNs.