A frequency selective surface (FSS) is designed and fabricated to resonate in the infrared. This IR FSS is designed using Periodic Method of Moments (PMM) software and is based on circuit-analog resonance of square loop conducting elements. The FSS is fabricated via electron beam lithography. The spectral characteristics of this surface are studied in the mid-infrared employing a spectral radiometer. The IR FSS may operate as an emissive narrowband source or reflective bandpass filter centered at a wavelength of 6.5μm, sharply cutting off short wavelength radiation and gradually filtering longer wavelengths. The addition of a superstrate layer, intended to further shape the FSS spectral signature, is also studied and the results discussed.
KEYWORDS: Magnetism, Weapons, Sensors, Magnetic sensors, Signal to noise ratio, Detection and tracking algorithms, Neural networks, Cell phones, Signal processing, Statistical analysis
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. False alarms were further reduced by analyzing the magnetic detector response by using a Joint Time Frequency Analysis digital signal processing technique. 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.
Passive FTIR remote sensing measurements were made to test real-time detection of an SF6 seeded stack plume using a probabilistic neural network (PNN) algorithm. The plume concentrations were determined using a classical least squares (CLS) algorithm and compared well with calculations using measured flow rates for the SF6 and the waste stream.
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