Abstract
This paper presents novel computing algorithms to generate tactical risk maps (TRM) based on the MECH (Monitor, Emplacement, and Command/Control in a Halo) model to evaluate locational values for attackers to launch improvised explosive device (IED) vs. direct fire (DF) attacks. Given a study area R, its proximity P can be mapped to explore noticeable characteristics associated with the attack locations. Within the distance constraints of the Halo, a simple optimization formula is proposed to support flexible representations of risk preferences of the attackers in ranking of locations for the M, C and E functions across R. Several case studies on major corridors find a significant number of attack locations were near or at local maxima of the measurement route exposure. It was found that IED sites tend to have good visibility and more uniform line-of-sight (LOS) distances. On the other hand, most DF locations are near the boundary of the viewshed suggesting careful selection of the sites to provide cover in the attack.
This work was supported in part by an ONR grant N00014-12-1-0531 and a National Defense Science and Engineering Graduate (NDSEG) fellowship. Any opinions, findings and conclusions or recommendations expressed in this material are the author(s) and do not necessarily reflect those of the sponsors.
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Lin, J., Qu, B., Wang, X., George, S.M., Liu, JC. (2015). Risk Management in Asymmetric Conflict: Using Predictive Route Reconnaissance to Assess and Mitigate Threats. In: Agarwal, N., Xu, K., Osgood, N. (eds) Social Computing, Behavioral-Cultural Modeling, and Prediction. SBP 2015. Lecture Notes in Computer Science(), vol 9021. Springer, Cham. https://doi.org/10.1007/978-3-319-16268-3_42
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DOI: https://doi.org/10.1007/978-3-319-16268-3_42
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