A concealed weapons detection technology was developed through the support of the National Institute of Justice (NIJ) to provide a non intrusive means for rapid detection, location, and archiving of data (including visual) of potential suspects and weapon threats. This technology, developed by the Idaho National Engineering and Environmental Laboratory (INEEL), has been applied in a portal style weapons detection system using passive magnetic sensors as its basis. This paper will report on enhancements to the weapon detection system to enable weapon classification and to discriminate threats from non-threats. Advanced signal processing algorithms were used to analyze the magnetic spectrum generated when a person passes through a portal. These algorithms analyzed multiple variables including variance in the magnetic signature from random weapon placement and/or orientation. They perform pattern recognition and calculate the probability that the collected magnetic signature correlates to a known database of weapon versus non-weapon responses. Neural networks were used to further discriminate weapon type and identify controlled electronic items such as cell phones and pagers.Analyzing the magnetic detector response by using a Joint Time Frequency Analysis digital signal processing technique further reduced false alarms. The frequency components and power spectrum for a given sensor response were derived. This unique fingerprint provided additional information to aid in signal analysis. This technology has the potential to produce major improvements in weapon detection and classification as a potential threat.
This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, make any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessariiy constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.
Critical infrastructure control systems face many challenges entering the 21st century, including natural disasters, cyber attacks, and terrorist attacks. Revolutionary change is underway to solve many existing issues, including gaining greater situational awareness and resiliency through embedding modeling and advanced control algorithms in smart sensors and control devices instead of in a central controller. To support design, testing, and component analysis, a flexible simulation and modeling capability is needed.Researchers at Idaho National Laboratory are developing and evaluating such a capability through their CIPRsim modeling and simulation framework.
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