UNISeC: Inspection, Separation, and Classification of Underwater Acoustic Noise Point Sources

Abstract

Advancements in oceanic research have resulted in a plethora of activities such as undersea oil/gas exploration, environmental monitoring, sonar-based coastal surveillance, which have each increased the acoustic noise levels in the ocean and have raised concerns in the scientific community about the effect of human-generated sounds on marine life. Knowledge of the statistical characteristics of noise sources and their spatial distribution is paramount for understanding the impact on marine life as well as for regulating and policing such activities. Furthermore, studies have shown that assuming the underwater noise probability density function to be Gaussian, exponential, or Weibull is often not valid; therefore, statistically profiling the sources of the ambient noise is also essential to improve the performance of acoustic communication systems in the harsh underwater environment. In this paper, a novel solution based on the blind source separation method is proposed to enable separation of underwater acoustic noise point sources in the presence of channel propagation multipath. The proposed Underwater Noise Inspection, Separation, and Classification (UNISeC) system performs several pre- and postprocessing steps forming a novel gray-box model. Assuming there is no prior information on the noise sources, UNISeC estimates the number of such sources as well as characterizes and classifies them via a recursive pilot-aided probing method while minimizing the environmental acoustic contamination. A correlation-based characterization as well as power spectral density based classification approaches are investigated to verify the proposed method. Several scenarios are also presented and evaluated in detail via simulations.