Modeling and Methodology for Incorporating Existing Technologies to Produce Higher Probabilities of Detecting Suicide Bombers

Abstract

Among the many weapons currently used by terrorist organizations against public welfare and coalition forces, human-born Improvised Explosive Devices (IEDs) present a significant threat. Commonly referred to as suicide bombers, these individuals enter crowded public areas in order to detonate their IED, inflicting lethal damage to the surrounding individuals. Constructed of non-standard parts and hidden under layers of clothing, these human-born IEDs go undetected until detonated. Currently, there are no detection systems that can identify suicide bombers at adequate standoff distances. The authors developed models and a methodology that examine current technologies to increase the probability of identifying a suicide bomber at a checkpoint or marketplace with an adequate standoff distance. The proposed methodology employs sensor technology incorporating unique detection threshold values. The authors analyze our proposed methodology utilizing a simulation model that provides both the probability of detecting a bomber and the probability of a false detection. These simulations will allow us to determine the threshold values for each sensor that result in the best probability of detection of a suicide bomber and allows for a small probability of false detections. Using experimentally “good” threshold values, the authors were able to drastically increase the probability of detection with a combination of radar and thermal imagery. In this paper, the main sensor is the hand-held radar