Improving the Speed and Safety of Airport Security Screening
So far, the research team has conducted three phases of tests. The tests were conducted at the University of Rhode Island’s explosives test range, which is part of Northeastern University’s Awareness and Localization of Explosives-Related Threats (ALERT) program, a multi-university DHS Center of Excellence. At the range, the team used the mass spectrometer to measure the air around nearly 100 different explosive samples concealed in various packaging configurations.
They collected several thousand measurements to understand how the different sample configurations influence the vapor signatures of concealed explosives. The team also plans to use these data to evaluate how data processing algorithms impact instrument and detection performance.
The end goal is to use the data that the team collected to build a list of requirements for developing an operational instrument. The DHS can use this list to decide how to proceed in contacting industry partners to develop the necessary technology and in coordinating their efforts with similar ones in Europe, led by the European Civil Aviation Conference. While there is much more work to do before the team can fully understand what it might take to build a non-contact detection system, they are hopeful.
“Developing and improving methods for explosives detection would streamline passenger experience and safety during airport security screening, while also supporting technology to remain resilient against new and evolving security threats,” Wrobel says.
Detecting from All Angles
The vapor detection research is just one example of the laboratory’s involvement in the NextGen ETD program. The Biological and Chemical Technologies Group is also involved in a project to create more effective swabs for security checkpoints and is exploring whether infrared lasers could be used to detect explosive particles on luggage.
“The core technology is called longwave infrared imaging,” says Bill Barney, who leads the infrared laser program. “It uses a laser that is scanned over a surface, and the scattered laser light has a spectrum to it. Some of the wavelengths of light in that spectrum are absorbed by explosives, which means the spectrum contains a fingerprint of the explosive that we can detect.”
However, the infrared method is complicated by clutter and false alarms. Some materials absorb the light in a similar way to explosives, so there is a need to be able to differentiate them. Barney and his team have turned to machine learning to solve this problem, which is better at unraveling complex data and making connections between data points that humans may not see.
“Last year, we were very successful in detecting low levels of explosives on our test samples, which is promising,” says Barney. “But there’s a lot of engineering and science left to do before you would be able to get this kind of system working in an airport.”
Rod Kunz, who is an associate leader of the Biological and Chemical Technologies Group, says that Lincoln Laboratory’s involvement in the NextGen ETD program fills an important niche.
“The main performers on this program are industry — companies that sell things to the TSA to use in airports,” Kunz says. “Our role is to try to understand if other technologies would work for airport needs, if there are other advanced concepts that should be sent to industry for them to respond to, or if there are directions that industry just doesn’t think are worth pursuing that the laboratory could be trying instead. We are trying to plug in the gaps that industry and the normal procurement processes are unable to do.”
Anne McGovern is a Science Writer/Editor at MIT Lincoln Laboratory. This story is reprinted with permission of MIT News.
