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OREOS: a new EO-IR modeling and simulation tool for US Coast Guard search and rescue applications

机译:OREOS:用于美国海岸警卫队搜救应用的新型EO-IR建模和仿真工具

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Georgia Tech has developed a new modeling and simulation tool that predicts both radar and electro-optical infrared (EO-IR) lateral range curves (LRCs) and sweep widths (SWs) under the Optimization of Radar and Electro-Optical Sensors (OREOS) program for US Coast Guard Search and Rescue (SAR) applications. In a search scenario when the location of the lost or overdue craft is unknown, the Coast Guard will conduct searches based upon standard procedure, personnel expertise, operational experience, and models. One metric for search planning is the sweep width, or integrated area under a LRC. Because a searching craft is equipped with radar and EO-IR sensor suites, the Coast Guard is interested in accurate predictions of sweep width for the particular search scenario. Here, we will discuss the physical models that make up the EO-IR portion of the OREOS code. First, Georgia Tech SIGnature (GTSIG) generates thermal signatures of search targets based upon the thermal and optical properties of the target and the environment; a renderer then calculates target contrast. Sensor information, atmospheric transmission, and the calculated target contrasts are input into NVESD models to generate probability of detection (PD) vs. slant range data. These PD vs. range values are then converted into LRCs by taking into account a continuous look search from a moving platform; sweep widths are then calculated. The OREOS tool differs from previous methods in that physical models are used to predict the LRCs and sweep widths at every step in the process, whereas heuristic methods were previously employed to generate final predictions.
机译:佐治亚理工学院开发了一种新的建模和仿真工具,可以根据雷达和光电传感器(OREOS)计划优化来预测雷达和光电红外(EO-IR)的横向距离曲线(LRC)和扫描宽度(SW)适用于美国海岸警卫队搜救(SAR)应用。在搜寻场景中,丢失或过期的船只的位置未知时,海岸警卫队将根据标准程序,人员专业知识,操作经验和模型进行搜索。搜索计划的一种度量标准是扫描宽度或LRC下的集成区域。由于搜索船配备了雷达和EO-IR传感器套件,因此海岸警卫队对特定搜索场景的扫宽精确预测感兴趣。在这里,我们将讨论构成OREOS代码的EO-IR部分的物理模型。首先,佐治亚理工学院技术小组(GTSIG)根据目标和环境的热学和光学特性生成搜索目标的热特征;然后,渲染器计算目标对比度。传感器信息,大气透射率和计算出的目标对比度输入到NVESD模型中,以生成检测概率(PD)与倾斜距离数据。然后考虑到来自移动平台的连续外观搜索,将这些PD与范围值转换为LRC。然后计算扫描宽度。 OREOS工具与以前的方法不同之处在于,物理模型用于预测过程中每个步骤的LRC和扫宽,而启发式方法以前曾用于生成最终预测。

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