By Topic

A Bayesian, Nonlinear Particle Filtering Approach for Tracking the State of Terrorist Operations

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Godfrey, G.A. ; Metron, Inc., Reston ; Cunningham, J. ; Tuan Tran

In this paper, we describe a novel approach to track the progress of suspected terrorist operations and to optimize courses of action to delay or disrupt these operations. The approach uses Monte Carlo sampling and Bayesian, nonlinear particle filtering to estimate the state (schedule) of a terrorist operation. The operation is specified in the form of a project management model (such as a Program Evaluation and Review Technique (PERT) model) with uncertain task durations. We describe the underlying algorithms for performing the estimation given a set of observables of variable quality, and evaluate the effectiveness of the techniques through a series of numerical experiments that include a wide range of data characteristics.

Published in:

Intelligence and Security Informatics, 2007 IEEE

Date of Conference:

23-24 May 2007