Using Generative Analysis for Homeland Security: Modeling the Possibilities and the Probabilities

Barry, Philip; Koehler, Matthew T. K.; Jacyna, Garry M.; Bergen-Hill, Tobin A.; Tierney, M.

doi:10.1109/ths.2008.4534500

Cited by 2 publications

(1 citation statement)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sensors are characterized using multi-dimensional lookup tables that are a function of environmental, spatial and threat variables. The flexibility of this lookup table approach accommodates a wide variety of sensor performance data sources including experimental measurements, physics-based modeling, and even hypothetical (Barry et al 2008). Given the specified sensor deployment pattern, SPLAT then analytically combines multi-dimensional sensor performances, scenario information, and pedestrian flow patterns to compute expected system performance.…”

Section: Introductionmentioning

confidence: 99%

Effects of stochastic traffic flow model on expected system performance

Hyland

Smith

2012

Proceedings Title: Proceedings of the 2012 Winter Simulation Conference (WSC)

View full text Add to dashboard Cite

In 2010 Naval Surface Warfare Center -Panama City Division (NSWC-PCD) developed a System Performance and Layered Analysis Tool (SPLAT) that evaluates candidate threat detection systems. Given a sensor deployment pattern, SPLAT combines sensor performances, scenario data, and pedestrian flow to analytically compute expected probability of detection (pd) and false alarm (pfa). Because the 2010 pedestrian flow model describes all possible trips through the detection area as straight-line paths, SPLAT can enumerate all possible trips and explicitly determine the maximum pd along each trip. NSWC-PCD's new 2011 flow model now accommodates stochastic pedestrian motion defined as a Markov process. However, stochastic flow modeling has created a combinatorial explosion; there are now too many paths to explicitly enumerate. Addressing this problem, NSWC-PCD has developed a unique expected maximum probability technique which approximates results obtained by enumerating all possible paths while still preserving spatial correlations created by sensor deployment patterns.

show abstract

Section: Introductionmentioning

confidence: 99%