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Dynamic 3-D chemical agent cloud mapping using a sensor constellation deployed on mobile platforms

机译:使用部署在移动平台上的传感器星座图进行动态3-D化学剂云映射

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The need for standoff detection technology to provide early Chem-Bio (CB) threat warning is well documented. Much of the information obtained by a single passive sensor is limited to bearing and angular extent of the threat cloud. In order to obtain absolute geo-location, range to threat, 3-D extent and detailed composition of the chemical threat, fusion of information from multiple passive sensors is needed. A capability that provides on-the-move chemical cloud characterization is key to the development of real-time Battlespace Awareness. We have developed, implemented and tested algorithms and hardware to perform the fusion of information obtained from two mobile LWIR passive hyperspectral sensors. The implementation of the capability is driven by current Nuclear, Biological and Chemical Reconnaissance Vehicle operational tactics and represents a mission focused alternative of the already demonstrated 5-sensor static Range Test Validation System (RTVS). The new capability consists of hardware for sensor pointing and attitude information which is made available for streaming and aggregation as part of the data fusion process for threat characterization. Cloud information is generated using 2-sensor data ingested into a suite of triangulation and tomographic reconstruction algorithms. The approaches are amenable to using a limited number of viewing projections and unfavorable sensor geometries resulting from mobile operation. In this paper we describe the system architecture and present an analysis of results obtained during the initial testing of the system at Dugway Proving Ground during BioWeek 2013.
机译:有充分的文献证明,需要采用对位检测技术来提供早期Chem-Bio(CB)威胁警告。单个被动传感器获得的许多信息仅限于威胁云的方位和角度范围。为了获得绝对地理位置,威胁范围,3D范围以及化学威胁的详细组成,需要融合来自多个无源传感器的信息。提供动态化学云表征的功能是实时战场意识开发的关键。我们已经开发,实施和测试了算法和硬件,以执行从两个移动LWIR无源高光谱传感器获得的信息的融合。该能力的实施是由当前的核,生物和化学侦察车的操作策略驱动的,并且是已展示的5传感器静态范围测试验证系统(RTVS)的任务重点替代方案。新功能包括用于传感器指向和姿态信息的硬件,可将其用于流和聚合,作为威胁特征描述数据融合过程的一部分。云信息是通过将2个传感器的数据提取到一组三角测量和断层扫描重建算法中生成的。该方法适合于使用有限数量的观看投影和由于移动操作而导致的不利的传感器几何形状。在本文中,我们描述了系统架构,并提供了在BioWeek 2013期间在Dugway试验场对系统进行初始测试期间获得的结果的分析结果。

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