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

被引:0
|
作者
Cosofret, Bogdan R. [1 ]
Konno, Daisei [1 ]
Rossi, David [1 ]
Marinelli, William J. [1 ]
Seem, Pete [1 ]
机构
[1] Phys Sci Inc, Andover, MA 01810 USA
关键词
computed tomography; passive LWIR imaging; CB threat detection; cloud mapping;
D O I
10.1117/12.2053755
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
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).(1) 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.
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收藏
页数:17
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