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Laser Line Scan Performance Prediction

机译:激光线扫描性能预测

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The effectiveness of sensors that use optical measurements for the laser detection and identification of subsurface mines is directly related to water clarity. The primary objective of the work presented here was to use the optical data collected by UUV (Slocum Glider) surveys of an operational areas to estimate the performance of an electro-optical identification (EOID) Laser Line Scan (LLS) system during RIMPAC 06, an international naval exercise off the coast of Hawaii. Measurements of optical backscattering and beam attenuation were made with a Wet Labs, Inc. Scattering Absorption Meter (SAM), mounted on a Rutgers University/Webb Research Slocum glider. The optical data universally indicated extremely clear water in the operational area, except very close to shore. The beam-c values from the SAM sensor were integrated to three attenuation lengths to provide an estimate of how well the LLS would perform in detecting and identifying mines in the operational areas. Additionally, the processed in situ optical data served as near-real-time input to the Electro-Optic Detection Simulator, ver. 3 (EODES-3; Metron, Inc.) model for EOID performance prediction. Both methods of predicting LLS performance suggested a high probability of detection and probability of identification. These predictions were validated by the actual performance of the LLS as the EOID system yielded imagery from which reliable mine identification could be made. Future plans include repeating this work in more optically challenging water types to demonstrate the utility of pre-mission UUV surveys of operational areas as a tactical decision aid for planning EOID missions.
机译:使用光学测量的传感器对激光检测和鉴定的传感器的有效性与水清晰度直接相关。这里呈现的工作的主要目标是使用由UUV(SloCum Glider)调查的光学数据进行操作区域,以估计Rimpac 06期间电光识别(Eoid)激光线扫描(LLS)系统的性能,夏威夷海岸的国际海军锻炼。光学反向散射和光束衰减的测量是用湿Labs,Inc。散射吸收计(SAM)进行的,安装在Rutgers大学/ Webb Research Slocum滑翔机上。除了非常接近岸边之外,光学数据普遍地指示了操作区域的极清晰。 SAM传感器的光束-C值被集成到三个衰减长度,以提供LLS在操作区域中的检测和识别地雷的程度的估计。另外,原位光学数据的处理用作电光检测模拟器Ver的近实时输入。 3(eodes-3; Metron,Inc。)模型用于出口性能预测。两种预测LLS性能的方法都表明了高概率的检测和识别概率。通过LLS的实际性能来验证这些预测,因为可以产生可靠的矿井识别的成像。未来的计划包括在更加光学挑战的水类型中重复这项工作,以展示运营领域的前期UUV调查的效用作为规划出口任务的战术决策援助。

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