New Proposal for the Detection of Concealed Weapons: Electromagnetic Weapon Detection for Open Areas

Terrorist groups, hijackers, and people hiding guns and knifes are a constant and increasing threat Concealed weapon detection (CWO) has turned into one of the greatest challenges facing the law enforcement community today. Current screening procedures for detecting concealed weapons such as handguns and knives are common in controlled access settings such as airports, entrances to sensitive buildings and public events. Unfortunately screening people in this way prior to entering controlled areas is ineffective in preventing some weapons from getting through and also produces bottle-necks in crowded environments. Also the screening technologies employed have a high rate of false alarms due to poor discrimination capability. A reliable CWD that is able to work in open areas and a robust method capable to discriminate between ferromagnetic weapons are necessary.

This thesis reviews recent developments in the area of CWO using largely electromagnetic methods. The advantages and disadvantages of these approaches are discussed and a new research direction in CWO is presented. This thesis proposes a cost-effective weapon detection system based on pulse induction technology which is able to work in open areas without invading individual privacy. This approach employs a uniform magnetic field generator to transmit pulses that cause eddy currents to flow in any metal object carried by people. The induced eddy currents decay exponentially following sudden changes in the exciting magnetic field with a characteristic decay time (time constant) that depends on the size, shape, and material composition of the object. The decay currents generate a secondary magnetic field and the rate-of-change ofthe field is detected by the sensors. This thesis introduces models based on finite element analysis (FEA) to study the potential use of the time constant as a signature for weapon discrimination. Experimental work is also presented that confirms the theoretical predictions obtained from FEA. It is shown that further work on signature extraction and signal processing needs to be done to build the weapon signature database necessary for classification.