Sensing and Identifying the Improvised Explosive Device Suicide Bombers: People Carrying Wires on Their Body
Abstract
We examined the use of radar to detect humans wearing detonation wires as part of a suicide vest in suicide bombings in an effort to stop the bombing. Dogaru et al. used numerical electromagnetic simulations to show ways to use radar backscatter to detect humans carrying weapons behind walls. We developed Numerical Electromagnetic Code (NEC) simulations for the radar cross-section of wire configurations appropriate to the human body and compared them to the radar cross-section simulations for the human body done by Dogaru et al. We also used GunnPlexer Doppler radar at 12.5 GHz to collect laboratory experimental data from standoff distances of 2-8 meters from the following: human subjects, human subjects wearing a wire loop, and human subjects wearing a simulated vest with wire loops. We performed numerous experiments with both horizontal and vertical polarization (HH and VV), analyzing the data after each experimental run. We developed metrics from examining our experimental data of the radar cross-sections that could be used in building models to more accurately find subjects wearing wires. We wanted a metric to provide us with better statistical detection rates. We found several metrics that improved our ability to detect persons wearing wires. We discovered our best metric was the VV/HH ratio of radar cross-section. From our empirical modeling, we found that the ratio for people wearing wires was statistically different from people without wires at a level of significance of a = 0.05. Using this metric, we built a Monte Carlo simulation model that generated a crowd of people and randomly picked those with wires on their person. We used our metric and a threshold value which we determined experimentally, to distinguish the persons with wires from those without wires. We found from our simulation that our metric provided a success rate of detecting persons wearing wires of approximately 83.4%, based on running 36,000 trial runs in Excel.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 01, 2010
- Accession Number
- ADA532537
Entities
People
- John Vesecky
- Kenneth Laws
- William P. Fox
Organizations
- Naval Postgraduate School