The EPSRC has awarded a £111,835 grant to a consortium of universities to develop a portable device to detect concealed weapons using combined electromagnetic and ultrasound sensors.
Airports and major event venues have scanners and portals installed which use microwaves, Terahertz waves (THz) or low-level X-rays to form images of concealed guns on a person. THz and microwaves can form clear body images by penetrating clothing, but moral and technical issues arise from the technology, particularly intrusion into privacy. The equipment is also not easily deployable.
The first aim of the research is to identify what sort of electromagnetic radiation best penetrates over a range of atmospheric conditions. A variety of electromagnetic radiation exists, including microwaves, light, infra-red, Terahertz and millimetre waves, which all differ in their ability to penetrate fabrics. The researchers will also investigate using ultrasound to detect metal objects concealed under clothing.
The body absorbs some forms of EMF whereas others are reflected back depending on the precise wavelength. The researchers will be looking for reflections off the surfaces of the gun in a similar manner to radar directing the bright echoes from ships and aircraft, while filtering out the lower level reflections from the human body
Guns are not the only objects which would be concealed about a person that could give these bright reflections at a remote sensing site. Mobile phones, leather wallets, pens and music players could also give detectable signals.
One of the goals of the research is to use features unique to a gun, such as the barrel and other cavities, to identify unique signatures in the reflected signals. The gun barrel can act as a resonant cavity, like a musical instrument, and the device could potentially detect these resonances remotely.
During the second phase of the investigation, the researchers will use a mix of the most effective bands of the electromagnetic spectrum together with ultrasound to develop a portable sensor that is effective at detecting guns remotely and is deployable by the police.
It is possible that different guns will produce different responses from the sensor, but the team will use neural network pattern recognition techniques similar to those used in automatic detection of number plates or handwriting to learn to recognise these particular responses.