UNISeC: Inspection, Separation, and Classification of Underwater Acoustic Noise Point Sources
UNISeC: Inspection, Separation, and Classification of Underwater Acoustic Noise Point Sources
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UNISeC：水下声学噪声点源的检查、分离和分类
DOI：
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发表时间：
2018
影响因子：
4.1
通讯作者：
D. Pompili
中科院分区：
文献类型：
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作者：
M. Rahmati;D. Pompili
海洋研究的进步催生了海底石油/天然气勘探、环境监测、基于声纳的海岸监视等大量活动，这些活动都增加了海洋中的噪声水平，并引起了科学界对人类产生的声音对海洋生物影响的担忧。了解噪声源的统计特征及其空间分布对于了解其对海洋生物的影响以及监管和监管此类活动至关重要。 Furthermore, studies have shown that assuming the underwater noise probability density function to be Gaussian, exponential, or Weibull is often not valid;因此，对环境噪声源进行统计分析对于提高恶劣水下环境中声学通信系统的性能也至关重要。本文提出了一种基于盲源分离方法的新颖解决方案，以在存在信道传播多径的情况下实现水声噪声点源的分离。拟议的水下噪声检查、分离和分类 (UNISeC) 系统执行多个预处理和后处理步骤，形成新颖的灰盒模型。假设没有关于噪声源的先验信息，UNISEC 会估计此类源的数量，并通过递归先导辅助探测方法对它们进行表征和分类，同时最大限度地减少环境声污染。研究了基于相关性的表征以及基于功率谱密度的分类方法来验证所提出的方法。还通过模拟详细介绍和评估了几种场景。
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.