Application of adaptive complementary ensemble local mean decomposition in underwater acoustic signal processing

Abstract

The aim of this study is to develop a signal decomposition method that not only overcomes the shortcomings of existing decomposition methods, but also can be further applied to complex underwater acoustic signal processing. Therefore, a novel Adaptive Complementary Ensemble Local Mean Decomposition (ACELMD) method was proposed. A simulated signal and real-world underwater acoustic signals from three marine mammals (white-sided dolphin, long-finned pilot whale, and harp seal) were employed to evaluate the decomposition performance, reliability, and practicality of the proposed ACELMD. Also, the decomposition results of the same simulated signal using ACELMD, Local Mean Decomposition (LMD), Ensemble Local Mean Decomposition (ELMD), and Complementary Ensemble Local Mean Decomposition (CELMD) methods were compared. All the results demonstrate the excellent decomposition performance of the proposed method. Moreover, compared with the other three methods, ACELMD effectively reduces the modal aliasing in the decomposition results, reduces the number of LMD executions, and is more inclusive of the white noise amplitude, all of which indicate its great potential in practical applications.