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Design and Tests of an Adaptive Triggering Method for Capturing Peak Samples in a Thin Phytoplankton Layer by an Autonomous Underwater Vehicle

机译:自主水下航行器捕获浮游植物薄层中峰值样品的自适应触发方法的设计和测试

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Thin layers of phytoplankton have an important impact on coastal ocean ecology. The high spatial and temporal variability of such layers makes autonomous underwater vehicles (AUVs) ideal for their study. At the Monterey Bay Aquarium Research Institute (MBARI, Moss Landing, CA), the authors have used an AUV for obtaining repeated high-resolution surveys of thin layers in Monterey Bay, CA. The AUV is equipped with ten “gulpers” that can capture water samples when some feature is detected. In this paper, the authors present an adaptive triggering method for an AUV to capture water samples at chlorophyll fluorescence peaks in a thin layer. The algorithm keeps track of the fluorescence background level and the peaks'' baseline in real time to ensure that detection is tuned to the ambient conditions. The algorithm crosschecks for concurrent high values of optical backscattering to ensure that sampling targets true particle peaks and not simply physiologically controlled fluorescence peaks. To let the AUV capture the thin layer''s peak without delay, the algorithm takes advantage of the vehicle''s sawtooth (i.e., yo-yo) trajectory: in one yo-yo cycle, the vehicle makes two crossings of the thin layer. On the first crossing, the vehicle detects the layer''s fluorescence peak and saves the peak height; on the second crossing, as the fluorescence measurement reaches the saved peak height (plus meeting additional timing and depth conditions), a sampling is triggered. Based on the thin layer''s vertical position in the vehicle''s yo-yo profiles, the algorithm selects the pair of detection and triggering crossings so as to minimize the spacing between them. We use the algorithm to postprocess a data set of 20 AUV missions in the 2005 Layered Organization in the Coastal Ocean (LOCO) Experiment in Monterey Bay, CA, and compare its performance with that of a threshold triggering method. In October 2009, the presented method was field tested in an AUV mission in nort-n-nhern Monterey Bay, CA.
机译:浮游植物的薄层对沿海海洋生态产生重要影响。这种层的高时空变化性使自主水下航行器(AUV)成为他们研究的理想选择。在蒙特利湾水族馆研究所(加利福尼亚州摩斯兰丁的MBARI),作者使用AUV进行了加利福尼亚州蒙特利湾的薄层重复高分辨率测量。 AUV配备了十个“吸嘴”,可以在检测到某些特征时捕获水样。在本文中,作者提出了一种自适应触发方法,用于AUV捕获薄层中叶绿素荧光峰处的水样。该算法实时跟踪荧光背景水平和峰值基线,以确保将检测调整到环境条件。该算法对光学反向散射的并发高值进行交叉检查,以确保采样的目标是真实的粒子峰,而不仅仅是生理上受控的荧光峰。为了让AUV毫不延迟地捕获薄层的峰,该算法利用了车辆的锯齿(即yo-yo)轨迹:在一个yo-yo循环中,车辆进行了两次薄层穿越层。在第一次穿越时,车辆会检测到该层的荧光峰并保存该峰的高度;在第二个交叉点,当荧光测量值达到保存的峰高(加上满足其他时间和深度条件)时,将触发采样。基于车辆的溜溜球轮廓中的薄层的垂直位置,该算法选择一对检测交叉点和触发交叉点,以最小化它们之间的间距。我们使用该算法对位于加利福尼亚州蒙特雷湾的2005年沿海海洋分层组织(LOCO)实验中20个AUV任务的数据集进行后处理,并将其性能与阈值触发方法进行比较。 2009年10月,本文提出的方法在加利福尼亚州北-北-北部蒙特雷湾的AUV任务中进行了现场测试。

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