Separation and classification of underwater acoustic sources
Separation and classification of underwater acoustic sources
复制标题
水下声源的分离与分类
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
D. Pompili
中科院分区：
文献类型：
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作者：
M. Rahmati;Parul Pandey;D. Pompili
海洋研究的进步导致了海底石油/天然气勘探、环境监测、基于声纳的沿海监测等过多的活动，所有这些都增加了海洋中的声学噪声水平，引起了科学界的关注。了解噪声源的统计特征及其空间分布对于了解噪声源对海洋生物的影响以及对这类活动的管制和监管十分重要。此外，研究表明，假设水下噪声为高斯噪声很少是有效的；因此，在线分析形成环境噪声的源对于提高恶劣水下环境中的水声通信系统的性能也是至关重要的。本文通过仿真研究了不同程度多径情况下，利用盲源分离(BSS)实现水声噪声源的实时分离及其基于功率谱密度(PSD)和已知噪声源功率谱密度的相关性进行分类的问题。目前正在开展工作，以验证结果，并利用从水下通信试验台收集的真实数据确定来源。
Advancements in oceanic research have resulted in a plethora of activities such as undersea oil/gas exploration, environmental monitoring, SONAR-based coastal surveillance, which all have increased the acoustic noise levels in the ocean, raising concerns in the scientific community. Knowledge about the statistical characteristics of noise sources and their spatial distribution is important for understanding the impact on marine life as well as for regulating and policing such activities. Furthermore, as studies have shown, assuming the underwater noise to be Gaussian is seldom valid; hence, online profiling of the sources forming the ambient noise is also essential to increase the performance of acoustic communication systems in the harsh underwater environment. In this paper, real-time separation of underwater acoustic noise sources via Blind Source Separation (BSS) in the presence of various degrees of multipath as well as their classification based on the coherence of their Power Spectral Density (PSD) and the PSD of known noise sources are studied via simulations. Work is currently being conducted to validate the results and to localize the sources using real data collected from underwater communication testbeds.