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Proceedings Paper

Implementation and testing of a sensor-netting algorithm for early warning and high confidence C/B threat detection
Author(s): Thomas Gruber; Larry Grim; Ryan Fauth; Brian Tercha; Chris Powell; Kristin Steinhardt
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Paper Abstract

Large networks of disparate chemical/biological (C/B) sensors, MET sensors, and intelligence, surveillance, and reconnaissance (ISR) sensors reporting to various command/display locations can lead to conflicting threat information, questions of alarm confidence, and a confused situational awareness. Sensor netting algorithms (SNA) are being developed to resolve these conflicts and to report high confidence consensus threat map data products on a common operating picture (COP) display. A data fusion algorithm design was completed in a Phase I SBIR effort and development continues in the Phase II SBIR effort. The initial implementation and testing of the algorithm has produced some performance results. The algorithm accepts point and/or standoff sensor data, and event detection data (e.g., the location of an explosion) from various ISR sensors (e.g., acoustic, infrared cameras, etc.). These input data are preprocessed to assign estimated uncertainty to each incoming piece of data. The data are then sent to a weighted tomography process to obtain a consensus threat map, including estimated threat concentration level uncertainty. The threat map is then tested for consistency and the overall confidence for the map result is estimated. The map and confidence results are displayed on a COP. The benefits of a modular implementation of the algorithm and comparisons of fused / un-fused data results will be presented. The metrics for judging the sensor-netting algorithm performance are warning time, threat map accuracy (as compared to ground truth), false alarm rate, and false alarm rate v. reported threat confidence level.

Paper Details

Date Published: 7 June 2011
PDF: 14 pages
Proc. SPIE 8064, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2011, 80640I (7 June 2011); doi: 10.1117/12.883189
Show Author Affiliations
Thomas Gruber, MESH, Inc. (United States)
Larry Grim, MESH, Inc. (United States)
Ryan Fauth, MESH, Inc. (United States)
Brian Tercha, MESH, Inc. (United States)
Chris Powell, MESH, Inc. (United States)
Kristin Steinhardt, MESH, Inc. (United States)


Published in SPIE Proceedings Vol. 8064:
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2011
Jerome J. Braun, Editor(s)

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