Multi-scale permutation Lempel-Ziv complexity and its application in feature extraction for Ship-radiated noise

Abstract

Permutation Lempel-Ziv complexity (PLZC) is a recently proposed method for analyzing signal complexity. However, PLZC only characterizes the signal complexity from single scale and has certain limitations. In order to overcome these shortcomings and improve the performance of feature extraction for underwater acoustic signal, this paper introduced coarse graining operation, proposed the multi-scale permutation Lempel-Ziv complexity (MPLZC), and proposed an automatic hybrid multi-feature extraction method for ship-radiated noise signal (S-S) based on multi-scale Lempel-Ziv complexity (MLZC), multi-scale permutation entropy (MPE) and MPLZC. The results of simulation and realistic experiments show that MPLZC can better reflect the change of signal complexity in detecting the dynamic change of signals, and more effectively distinguish white noise, pink noise and blue noise than MPE and MLZC; compared with the three feature extraction methods based on MLZC, MPE and MPLZC respectively, the proposed method has the highest recognition rates of six S-Ss under the same number of features, and the recognition rate reaches 100% when the number of features is 5; the introduction of MPLZC significantly improves the performance for ship-radiated noise signal of the automatic hybrid multi-feature extraction method. It is indicated that the proposed method, as a new underwater acoustic technology, is valid in other underwater acoustic signals.