Benchmarking sensor fusion capabilities of an integrated instrumentation package

Emma Cotter, Paul Murphy, Brian Polagye
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引用次数: 12

Abstract

Quantifying and mitigating environmental risks presented by marine energy conversion systems requires a variety of sensors (active acoustic, passive acoustic, and optical). The operation of these sensors must satisfy three directives to be effective: (1) do not alter the environment through operation of sensors; (2) capture rare events; and (3) do not accrue unmanageable volumes of low-value data. This requires integrating sensors into a single package, rather than operating them independently. The Adaptable Monitoring Package is an integrated instrumentation package that combines a multibeam sonar, acoustic camera, current profiler, optical cameras, and an array of hydrophones. The capabilities and limitations of the AMP sensors were benchmarked using cooperative targets, and real-time target tracking and detection was used to detect opportunistic targets (e.g., diving birds, seals). During an initial deployment, automatic detection of opportunistic targets achieved a 58% true positive rate and a 99% true negative rate (100% corresponding to an ideal system in both cases). In post-processing, target tracking data were used to evaluate automatic target classification capabilities using a k-nearest neighbor algorithm. Results suggest that real-time target classification should be possible and enable integrated instrumentation systems to meet the monitoring needs of marine energy deployments.

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基准传感器融合能力的集成仪器包
量化和减轻海洋能源转换系统带来的环境风险需要各种传感器(主动声学、被动声学和光学)。这些传感器的操作必须满足三个指令才能有效:(1)不通过传感器的操作改变环境;(2)捕捉罕见事件;(3)不会积累大量难以管理的低价值数据。这需要将传感器集成到一个单一的封装中,而不是单独操作它们。适应性监测包是一个集成的仪器包,包括多波束声纳、声学相机、电流分析器、光学相机和一系列水听器。利用合作目标对AMP传感器的能力和局限性进行基准测试,并使用实时目标跟踪和检测来检测机会性目标(如潜水鸟、海豹)。在初始部署期间,自动检测机会目标的真阳性率为58%,真阴性率为99%(100%对应于两种情况下的理想系统)。在后处理中,利用目标跟踪数据,采用k近邻算法评估自动目标分类能力。结果表明,实时目标分类应该是可能的,并使集成仪器系统能够满足海洋能源部署的监测需求。
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