Real-time reporting of ecosystem metrics from acoustic sensors on gliders

海洋水层生态系统的评估对取样提出了一些方法上的挑战：需要高的空间和时间分辨率、同步的生物和环境信息以及对取样设备的行为反应。主动声学技术现在通常用于解析海洋生物（从浮游动物到鱼类和较大的生物）的高分辨率分布，通常来自大型研究船。自主水下航行器（AUV），如水下滑翔机，携带与生态系统研究有关的主动声学传感器的能力，直到最近才被探索。然而，由于它们的高数据量创建，这些传感器目前在本地存储数据，以便在平台恢复后进行检索和分析。滑翔机最大的吸引力之一是将它们引导到感兴趣的区域并实时接收数据。目前简单的回声测深仪集成到滑翔机产生256字节字符串每次ping（ping率为0.25 - 1赫兹）。更新、更复杂的宽带回声探测器的数量级更高。这些数据需要处理和约束到滑翔机上的度量（声学区域后向散射强度，垂直分布，聚合），然后可以传输回来。从而使生态系统描述符能够从滑翔机/AUV实时传输回海岸，并实现这些平台用于与渔业管理和影响评估相关的生态系统研究的能力。这个博士项目将与最先进的声学仪器合作，开发船上处理能力，以实现这一挑战。当声学数据显示在回声图中时，浮游动物和鱼类的聚集和分散是明显的，形成了不同的空间格局，如鱼群、浅滩和扩散云。回波跟踪分类技术使这一复杂的信息得以简化。该项目将利用来自宽带和窄带回声测深仪的现有声学数据集，开发先进的压缩方法，使相关信息能够通过低带宽信道传输，从而影响使命。考虑到机载处理有限，我们最初的方法将是最先进的机器学习方法，例如深度神经网络（目前我们正在使用唇读），这些方法经过昂贵的训练（离线），但几乎不需要运行时计算。我们的目标是开发一个压缩层次结构，其中最需要的信息首先发送，其次是细微差别。学生将与滑翔机和声学仪器制造商合作，将开发的指标和处理能力实施到滑翔机部署中。BAS和UEA在许多环境中部署滑翔机（例如北大西洋，南极），预计学生将利用其中一个机会来实施他们的技术。

The assessment of marine pelagic ecosystems poses a number of methodological challenges for sampling: the requirement for high spatial and temporal resolution, concurrent biological and environmental information, and behavioural responses to sampling equipment. Active acoustic techniques are now routinely used to resolve the high resolution distribution of marine organisms (from zooplankton to fish and larger organisms), typically from large research ships. The ability of Autonomous Underwater Vehicles (AUVs) such as underwater gliders to carry active acoustic sensors pertinent to ecosystem research has only recently been explored. However, due to their high data volume creation, these sensors currently store data locally for retrieval and analysis once the platform is recovered. One of the large appeals of gliders is directing them to regions of interest and receiving data in real-time. The current simple echosounder integrated into gliders generates 256 byte strings per ping (ping rate of 0.25 - 1 Hz). Newer, more complex wideband echosounders an order of magnitude more. These data need processing and constraining into metrics (acoustic area backscattering strength, vertical distribution, aggregation) onboard the glider, which can then be transmitted back. Thus enabling ecosystem descriptors to be transmitted back to shore from the glider/AUV in real-time and realizing these platforms capabilities for ecosystem research relevant to both fisheries management and impact assessments.This PhD project will work with state-of-the-art acoustic instruments to develop on-board processing capabilities to realize this challenge.When acoustic data are displayed in echograms, aggregations and scattering of zooplankton and fish are evident forming diverse spatial patterns such as schools, shoals and diffuse clouds. Echotrace classification techniques enable this complex information to be simplified. This project will use existing acoustic datasets from wideband and narrowband echosounders to develop advanced compression methods that will allow the pertinent information to be transmitted down the low-bandwidth channel and hence influence the mission. Given that onboard processing is limited, our initial approach will be to state-of-the-art machine learning methods such as Deep Neural Networks (currently being used by us in lip-reading) which are trained expensively (offline) but need little run-time computation. Our aim is to develop a compression hierarchy in which the most needed information is sent first, followed by the nuances. The student will work with glider and acoustic instrument manufacturers to implement the developed metrics and processing capabilities into a glider deployment. BAS and UEA deploy gliders in a number of environments (e.g. North Atlantic, Antarctic) and it is envisaged the student will use one of these opportunities to implement their technique.