A Complexity-Based Approach for the Detection of Weak Signals in Ocean Ambient Noise

Shashidhar Siddagangaiah,Xiao Chen,Qunfei Zhang,Xijing Guo,Yaan Li,Kunde Yang,Yixin Yang

doi:10.3390/e18030101

Abstract

There are numerous studies showing that there is a constant increase in the ocean ambient noise level and the ever-growing demand for developing algorithms for detecting weak signals in ambient noise. In this study, we utilize dynamical and statistical complexity to detect the presence of weak ship noise embedded in ambient noise. The ambient noise and ship noise were recorded in the South China Sea. The multiscale entropy (MSE) method and the complexity-entropy causality plane (C-H plane) were used to quantify the dynamical and statistical complexity of the measured time series, respectively. We generated signals with varying signal-to-noise ratio (SNR) by varying the amplification of a ship signal. The simulation results indicate that the complexity is sensitive to change in the information in the ambient noise and the change in SNR, a finding that enables the detection of weak ship signals in strong background ambient noise. The simulation results also illustrate that complexity is better than the traditional spectrogram method, particularly effective for detecting low SNR signals in ambient noise. In addition, complexity-based MSE and C-H plane methods are simple, robust and do not assume any underlying dynamics in time series. Hence, complexity should be used in practical situations.

Highlights

The background sound in the ocean is called ambient noise
It is evident from the spectrogram that, when signal-to-noise ratio (SNR) = ́14.4 dB, almost the entire prominent signature of ship tracks is masked by ambient noise
We evaluated statistical and dynamical untapped source of dynamical information

Summary

Introduction

Ambient noise is constituted by contributions from numerous natural and anthropogenic source These sounds blend to produce a continuum of noise against which acoustic receivers must be used to detect a signal. The received signals are perturbed by varying ambient noise levels with constantly changing factors such as intensity of rainfall, wind speed and its direction, anthropogenic sources, and human-related noises [1,2,3]. These processes exhibit multiple spatial and temporal variations, strongly affecting ocean acoustic signals. This has negative implications for mammalian marine wild life [8,9]

Methods

Results

Conclusion