A promising approach to stand-off detection of explosive traces is using resonance Raman spectroscopy with Deep-ultraviolet (DUV) light. The DUV region offers two main advantages: strong explosive signatures due to resonant and. 4 enhancement of Raman cross-section, and lack of fluorescence and solar background. For DUV Raman spectroscopy, continuous-wave (CW) or quasi-CW lasers are preferable to high peak powered pulsed lasers because Raman saturation phenomena and sample damage can be avoided. In this work we present a very compact DUV source that produces greater than 1 mw of CW optical power. The source has high optical-to-optical conversion efficiency, greater than 5 %, as it is based on second harmonic generation (SHG) of a blue/green laser source using a nonlinear crystal placed in an external resonant enhancement cavity. The laser system is extremely compact, lightweight, and can be battery powered. Using two such sources, one each at 236.5 nm and 257.5 nm, we are building a second generation explosive detection system called Dual-Excitation-Wavelength Resonance-Raman Detector (DEWRRED-II). The DEWRRED-II system also includes a compact dual-band high throughput DUV spectrometer, and a highly-sensitive detection algorithm. The DEWRRED technique exploits the DUV excitation wavelength dependence of Raman signal strength, arising from complex interplay of resonant enhancement, self-absorption and laser penetration depth. We show sensor measurements from explosives/precursor materials at different standoff distances.

Long range identification using facial recognition is being pursued as a valuable surveillance tool. The capability to perform this task covertly and in total darkness greatly enhances the operators' ability to maintain a large distance between themselves and a possible hostile target. An active-SWIR video imaging system has been developed to produce high-quality long-range night/day facial imagery for this purpose. Most facial recognition techniques match a single input probe image against a gallery of possible match candidates. When resolution, wavelength, and uncontrolled conditions reduce the accuracy of single-image matching, multiple probe images of the same subject can be matched to the watch-list and the results fused to increase accuracy. If multiple probe images are acquired from video over a short period of time, the high correlation between the images tends to produce similar matching results, which should reduce the benefit of the fusion. In contrast, fusing matching results from multiple images acquired over a longer period of time, where the images show more variability, should produce a more accurate result. In general, image variables could include pose angle, field-of-view, lighting condition, facial expression, target to sensor distance, contrast, and image background. Long-range short wave infrared (SWIR) video was used to generate probe image datasets containing different levels of variability. Face matching results for each image in each dataset were fused, and the results compared.

Raman spectroscopy is a widely used spectroscopic technique with a number of applications. During the past few years, we explored the use of simultaneous multiple-excitation-wavelengths (MEW) in resonance Raman spectroscopy. This approach takes advantage of Raman band intensity variations across the Resonance Raman spectra obtained from two or more excitation wavelengths. Amplitude variations occur between corresponding Raman bands in Resonance Raman spectra due to complex interplay of resonant enhancement, self-absorption and laser penetration depth. We have developed a very sensitive algorithm to estimate concentration of an analyte from spectra obtained using the MEW technique. The algorithm uses correlations and least-square minimization approach to calculate an estimate for the concentration. For two or more excitation wavelengths, measured spectra were stacked in a two dimensional matrix. In a simple realization of the algorithm, we approximated peaks in the ideal library spectra as triangles. In this work, we present the performance of the algorithm with measurements obtained from a dual-excitation-wavelength Resonance Raman sensor. The novel sensor, developed at WVHTCF, detects explosives from a standoff distance. The algorithm was able to detect explosives with very high sensitivity even at signal-to-noise ratios as low as ~1.6. Receiver operating characteristics calculated using the algorithm showed a clear benefit in using the dual-excitation-wavelength technique over single-excitation-wavelength techniques. Variants of the algorithm that add more weight to amplitude variation information showed improved specificity to closely resembling spectra.

Covert, long-range, night/day identification of stationary human subjects using face recognition has been previously demonstrated using the active-SWIR Tactical Imager for Night/Day Extended-Range Surveillance (TINDERS) system. TINDERS uses an invisible, eye-safe, SWIR laser illuminator to produce high-quality facial imagery under conditions ranging from bright sunlight to total darkness. The recent addition of automation software to TINDERS has enabled the autonomous identification of moving subjects at distances greater than 100 m. Unlike typical cooperative, short range face recognition scenarios, where positive identification requires only a single face image, the SWIR wavelength, long distance, and uncontrolled conditions mean that positive identification requires fusing the face matching results from multiple captured images of a single subject. Automation software is required to initially detect a person, lock on and track the person as they move, and select video frames containing high-quality frontal face images for processing. Fusion algorithms are required to combine the matching results from multiple frames to produce a high-confidence match. These automation functions will be described, and results showing automated identification of moving subjects, night and day, at multiple distances will be presented.

Deep-ultraviolet resonance Raman spectroscopy (DUVRRS) is a promising approach to stand-off detection of explosive traces due to large Raman cross-section and background free signatures. In order to design an effective sensor, one must be able to estimate the signal level of the DUVRRS signature for solid-phase explosive residues. The conventional approach to signal estimation uses scattering cross-sections and molar absorptivity, measured on solutions of explosives dissolved in an optically-transparent solvent. Only recently have researchers started to measure solid-state cross-sections. For most solid-phase explosives and explosive mixtures, neither the DUV Raman scattering cross sections nor the optical absorption coefficient are known, and they are very difficult to separately measure. Therefore, for a typical solid explosive mixture, it is difficult to accurately estimate Raman signal strength using conventional approaches. To address this issue, we have developed a technique to measure the Raman scattering strength of optically-thick (opaque) materials, or “Raman Albedo”, defined as the total power of Raman-scattered light per unit frequency per unit solid angle divided by the incident power of the excitation source. We have measured Raman Albedo signatures for a wide range of solid explosives at four different DUV excitation wavelengths. These results will be presented, and we will describe the use of Raman Albedo measurements in the design and current construction of a novel stand-off explosive sensor, based on dual-excitation-wavelength DUVRRS.

The capability to detect, observe, and positively identify people at a distance is important to numerous security and defense applications. Traditional solutions for human detection and observation include long-range visible imagers for daytime and thermal infrared imagers for night-time use. Positive identification, through computer face recognition, requires facial imagery that can be repeatably matched to a database of visible facial signatures (i.e. mug shots). Nighttime identification at large distance is not possible with visible imagers, due to lack of light, or with thermal infrared imagers, due to poor correlation with visible facial imagery. An active-SWIR imaging system was developed that is both eye-safe and invisible, capable of producing close-up facial imagery at distances of several hundred meters, even in total darkness. The SWIR facial signatures correlate well to visible signatures, allowing for biometric face recognition night or day. Night-time face recognition results for several distances will be presented. Human detection and observation results at larger distances will also be presented. Example signatures will be presented and discussed.

Deep-ultraviolet resonance Raman spectroscopy (DUVRRS) is a promising approach to stand-off detection of explosive traces due to: 1) resonant enhancement of Raman cross-section, 2) λ-4-cross-section enhancement, and 3) fluorescence and solar background free signatures. For trace detection, these signal enhancements more than offset the small penetration depth due to DUV absorption. A key challenge for stand-off sensors is to distinguish explosives, with high confidence, from a myriad of unknown background materials that may have interfering spectral peaks. To address this, we are developing a stand-off explosive sensor using DUVRRS with two simultaneous DUV excitation wavelengths. Due to complex interplay of resonant enhancement, self-absorption and laser penetration depth, significant amplitude variation is observed between corresponding Raman bands with different excitation wavelengths. These variations with excitation wavelength provide an orthogonal signature that complements the traditional Raman signature to improve specificity relative to single-excitation-wavelength techniques. As part of this effort, we are developing two novel CW DUV lasers, which have potential to be compact, and a compact dual-band high throughput DUV spectrometer, capable of simultaneous detection of Raman spectra in two spectral windows. We have also developed a highly sensitive algorithm for the detection of explosives under low signal-to-noise situations.

Positive identification of personnel from a safe distance is a long-standing need for security and defense applications. Advances in computer face recognition have made this a reliable means of identification when facial imagery of sufficient resolution is available to be matched against a database of mug shots. Long-range identification at night requires that the face be actively illuminated; however, for visible and NIR illumination, the intensity required to produce high-resolution long-range imagery typically creates an eye-safety hazard. SWIR illumination makes active-SWIR imaging a promising approach to long-range night-time identification. We will describe an active-SWIR imaging system that is being developed to covertly detect, track, zoom in on, and positively identify a human target, night or day, at hundreds of meters range. The SWIR illuminator pans, tilts, and zooms with the imager to always just fill the imager field of view. The illuminator meets Class 1 eye-safety limits (safe even with magnifying optics) at the intended target, and meets Class 1M eye-safety limits (safe to the naked eye) at point-blank range. Close-up night-time facial imagery will be presented along with experimental face recognition performance results for matching SWIR imagery to a database of visible mug shots at distance.

The capability to positively and covertly identify people at a safe distance, 24-hours per day, could provide a valuable advantage in protecting installations, both domestically and in an asymmetric warfare environment. This capability would enable installation security officers to identify known bad actors from a safe distance, even if they are approaching under cover of darkness. We will describe an active-SWIR imaging system being developed to automatically detect, track, and identify people at long range using computer face recognition. The system illuminates the target with an eye-safe and invisible SWIR laser beam, to provide consistent high-resolution imagery night and day. SWIR facial imagery produced by the system is matched against a watch-list of mug shots using computer face recognition algorithms. The current system relies on an operator to point the camera and to review and interpret the face recognition results. Automation software is being developed that will allow the system to be cued to a location by an external system, automatically detect a person, track the person as they move, zoom in on the face, select good facial images, and process the face recognition results, producing alarms and sharing data with other systems when people are detected and identified. Progress on the automation of this system will be presented along with experimental night-time face recognition results at distance.

Deep-ultraviolet resonance Raman spectroscopy (DUVRRS) is a potential candidate for stand-off detection of explosives. A key challenge for stand-off sensors is to distinguish explosives, with high confidence, from a myriad of unknown background materials that may have interfering spectral peaks. To address this, we have investigated a new technique that simultaneously detects Raman spectra from multiple DUV excitation wavelengths. Due to complex interplay of resonant enhancement, self-absorption and laser penetration depth, significant intensity variation is observed between corresponding Raman bands with different excitation wavelengths. These variations with excitation wavelength provide a unique signature that complements the traditional Raman signature to improve specificity relative to single-excitation-wavelength techniques. We have measured these signatures for a wide range of explosives using amplitude-calibrated Raman spectra, obtained sequentially by tuning a frequency-doubled Argon laser to 229, 238, 244 and 248 nm. For nearly all explosives, these signatures are found to be highly specific. An algorithm is developed to quantify the specificity of this technique. To establish the feasibility of this approach, a multi-wavelength DUV source, based on Nd:YAG harmonics and hydrogen Raman shifting, and a compact, high throughput DUV spectrometer, capable of simultaneous detection of Raman spectra in multiple spectral windows, are being investigated experimentally.

We have developed a new technique that uses Raman spectra obtained from multiple DUV excitation wavelengths for explosive detection. The strong dependence on excitation wavelength of the Raman signal provides a unique signature, improving specificity.

We present a strategy for integrating ‘agile electronics’ within optical network devices to create low power, low latency, and compact WDM LAN components suitable for avionics applications. We present the development of a multi-channel DWDM transmitter and the feasibility of an optical-electrical-optical (OEO) wavelength router for realization as optical network elements (ONE) within a WDM LAN.

We measured the 229 nm absolute ultraviolet (UV) Raman cross-sections of the explosives trinitrotoluene (TNT), pentaerythritol tetranitrate (PETN), cyclotrimethylene-trinitramine (RDX), the chemically related nitroamine explosive HMX, and ammonium nitrate in solution. The 229 nm Raman cross-sections are 1000-fold greater than those excited in the near-infrared and visible spectral regions. Deep UV resonance Raman spectroscopy enables detection of explosives at parts-per-billion (ppb) concentrations and may prove useful for stand-off spectroscopic detection of explosives.

Pressurized rail tank cars transport large volumes of volatile liquids and gases throughout the country, much of which is hazardous and/or flammable. These gases, once released in the atmosphere, can wreak havoc with the environment and local populations. We developed a system which can non-intrusively and non-invasively detect and locate pinholesized leaks in pressurized rail tank cars using acoustic sensors. The sound waves from a leak are produced by turbulence from the gas leaking to the atmosphere. For example, a 500 mu n hole in an air tank pressurized to 689 kPa produces a broad audio frequency spectrum with a peak near 40 kHz. This signal is detectable at 10 meters with a sound pressure level of 25 dB. We are able to locate a leak source using triangulation techniques. The prototype of the system consists of a network of acoustic sensors and is located approximately 10 meters from the center of the rail-line. The prototype has two types of acoustic sensors, each with different narrow frequency response band: 40 kHz and 80 kHz. The prototype is connected to the Internet using WiFi (802.1 Ig) transceiver and can be remotely operated from anywhere in the world. The paper discusses the construction, operation and performance of the system.

Pressurized rail tank cars transport large volumes of volatile liquids and gases throughout the country, much of which is hazardous and/or flammable. Our group is developing a trackside inspection system for these tank cars. It consists of five narrow frequency band pressure sensors with center frequencies of 40 and 75 kHz, a broad band microphone for sound normalization and three video cameras. In addition, a 5 cm times 5 cm NaI(Tl) radiation detector provides radiological data on the passing trains every 60 seconds. During operation, an audio frequency spectrum is associated with each frame of the video camera as the train passes by the system at normal speeds, and the spectra are inspected for high frequency sounds associated with leaks. A 10 m tall tower houses the system positioned approximately 10 m from the center of a rail line and siding located in Bowling Green, KY (USA). The system is controlled by a website and server located at the tower and the Internet connection utilizes WiFi (802.11 g) radios.