Detection, Identification and Monitoring (DIM) of hazardous chemical, biological, and radiological material is a critical component to Situational Awareness. Timely generated information just before and following a positive detection will lead to the most appropriate Course of Action (COA). The Technical Cooperation Program (TTCP) orchestrated a series of experiments to understand the operational limitations of new technologies in a Contested Urban Environment (CUE). One of the urban challenges occurred in Montreal, Canada in September 2018 where several technologies including a suite of biological DIM sensors were deployed. The urban environment adds complexity to the already challenging DIM task with potential line-of-sight limitations, changing wind conditions, complex communication spectrum, limited maneuverability, etc. The biological DIM suite deployed at this event included standoff, fixed point, mobile point and sampling, and identification sensing assets. The event revealed that the combination of various types of technologies might increase the overall system effectiveness. BioSense, a standoff technology, demonstrated its capacity to perform bio threat surveillance in urban environments having different constraints: short to long ranges; day and night operation; presence of various background sources; multiple surveillance areas without a deployment site having a line-of-sight on all of them and GPS-denied environment. The dedicated Chemical/Biological (CB) Sensor Data Viewer generated an integrated view of the available information from all sensors in real-time and provided a subset of this information to a central common operating software. The Class I mini UAS was equipped with an optical particle counter and filter collector membrane that was targeted to the appropriate location based on the cloud detected by the standoff sensor; and then, material classification obtained in near-real-time from the standoff spectral Laser Induced Fluorescence (LIF) interrogation was confirmed by simple post-processing of samples collected by the UAS.
Detection, Identification and Monitoring (DIM) of biological material is critical to enhancing Situational Awareness (SA) in a timely manner, supporting decisions, and enabling the endangered force to take the most appropriate actions in a recognized CB environment. An optimum Bio DIM capability would include both point sensors to provide local monitoring and sampling for confirmatory ID of the material, and standoff sensors to provide wide-area monitoring from a distance, increasing available response time and enhancing SA. In June 2015, a Canadian team co-deployed a point (VPBio) and a standoff (BioSense) bio sensor during the international S/K Challenge II event, at Dugway Proving Ground (DPG), USA. The co-deployment of the point and standoff sensors allowed the assessment of their respective strengths and limitations with regards to Bio DIM and SA in a realistic CB environment. Moreover, the initial hypothesis stating the existence of valuable leverages between the two sensors in a context of Bio DIM was confirmed. Indeed, the spatial limitation of the point sensor was overcome with the wide area coverage of the standoff technology. In contrast, the sampling capability of the point sensor can allow confirmatory identification of the detected material. Additionally, in most scenarios, the combined results allowed an increase in detection confidence. In conclusion, the demonstration of valuable leverages between point and standoff sensors in a context of Bio DIM was made, confirming the mitigation effect of co-deploying these systems for bio surveillance.
KEYWORDS: Sensors, Signal detection, Detection and tracking algorithms, Photon counting, Interference (communication), Aerosols, Signal to noise ratio, Signal processing, Databases, Atmospheric modeling
Photon counting technologies are developed and could be used in the future to measure the return from laser induced fluorescence. Currently, the spectral detection of light emitted by fluorescing aerosols is performed with ICCD, Intensified Charge Coupled Device. The signal to noise ratio of ICCD devices is smaller by a factor of √2compared to photon counting devices having the same sensitivity. We studied the impact of this difference of signal to noise ratio on the capability of multivariate detection and classification algorithms to operate on various conditions. Signal simulations have been performed to obtain ROC (Receiver Operation Characteristics) Curves and Confusion Matrix to obtain the detection performance and the ability of algorithms to discriminate a potential source from another. Two detection algorithms are used, the Integrated Laser Induced Fluorescence(ILIF) and the Matched Filter. For the classification, three algorithms are used, the Adaptive Matched Filter (AMF), the Adaptive Coherent Estimator (ACE) and the Adaptive Least Squares (ALS). The best algorithm for detection is the AMF using the signature of the material present in a cloud, the ILIF detector performs very well. For the classification, the three algorithms are surprisingly giving the same results for the same data. The classification performs better if the distance between the signatures recorded in a database is important. The performance of the detector and of the classificator improves with an increase of the signal to noise ratio and is consistently and significantly better for the photon counting compared to ICCD.
A standoff sensor called BioSense was developed to demonstrate the capacity to map, track and classify bioaerosol clouds from a distant range and over wide area. The concept of the system is based on a two steps dynamic surveillance: 1) cloud detection using an infrared (IR) scanning cloud mapper and 2) cloud classification based on a staring ultraviolet (UV) Laser Induced Fluorescence (LIF) interrogation. The system can be operated either in an automatic surveillance mode or using manual intervention. The automatic surveillance operation includes several steps: mission planning, sensor deployment, background monitoring, surveillance, cloud detection, classification and finally alarm generation based on the classification result. One of the main challenges is the classification step which relies on a spectrally resolved UV LIF signature library. The construction of this library relies currently on in-chamber releases of various materials that are simultaneously characterized with the standoff sensor and referenced with point sensors such as Aerodynamic Particle Sizer® (APS). The system was tested at three different locations in order to evaluate its capacity to operate in diverse types of surroundings and various environmental conditions. The system showed generally good performances even though the troubleshooting of the system was not completed before initiating the Test and Evaluation (T&E) process. The standoff system performances appeared to be highly dependent on the type of challenges, on the climatic conditions and on the period of day. The real-time results combined with the experience acquired during the 2012 T & E allowed to identify future ameliorations and investigation avenues.
Threats associated with bioaerosol weapons have been around for several decades and have been mostly associated with
terrorist activities or rogue nations. Up to the turn of the millennium, defence concepts against such menaces relied
mainly on point or in-situ detection technologies. Over the last 10 years, significant efforts have been deployed by
multiple countries to supplement the limited spatial coverage of a network of one or more point bio-detectors using lidar
technology. The addition of such technology makes it possible to detect within seconds suspect aerosol clouds over area
of several tens of square kilometers and track their trajectories. These additional capabilities are paramount in directing
presumptive ID missions, mapping hazardous areas, establishing efficient counter-measures and supporting subsequent
forensic investigations. In order to develop such capabilities, Defence Research and Development Canada (DRDC) and
the Chemical, Biological, Radiological-Nuclear, and Explosives Research and Technology Initiative (CRTI) have
supported two major demonstrations based on spectrally resolved Laser Induced Fluorescence (LIF) lidar: BioSense,
aimed at defence military missions in wide open spaces, and SR-BioSpectra, aimed at surveillance of enclosed or semienclosed
wide spaces common to defence and public security missions. This article first reviews briefly the modeling
behind these demonstration concepts. Second, the lidar-adapted and the benchtop bioaerosol LIF chambers (BSL1),
developed to challenge the constructed detection systems and to accelerate the population of the library of spectral LIF
properties of bioaerosols and interferents of interest, will be described. Next, the most recent test and evaluation (T&E)
results obtained with SR-BioSpectra and BioSense are reported. Finally, a brief discussion stating the way ahead for a
complete defence suite is provided.
Defence R&D Canada (DRDC) has developed, by the end of the 90s, a standoff bioaerosol sensor based on intensified
range-gated spectrometric detection of Laser Induced Fluorescence (LIF). This sensor called SINBAHD demonstrated
the capability to detect and characterize bioaerosols from a stand-off position. The sensor sensitivity and false alarm rate
directly depend on the background characteristics since these later will dictate the threshold levels to be used. SINBAHD
was used to characterize the background aerosols in a maritime environment close to Halifax, Canada in May 2008. The
characterization of the LIF signal from the background aerosols included spectral, temporal and spatial aspects over 8
nights of continuous data collection. The local environmental conditions in addition to the aerosol concentration and
particle size distribution were recorded during the entire trial period. From the 64 LIF trials, only five showed specific
spectral features. The spectral variability was encountered either at short range, thus closer to the shore, or during a night
having a specific prevalent wind direction. Indeed, the detected anomalies were in most cases directly related to the
climatic conditions. The integrated LIF signal was also processed to assess the use of LIF intensity to identify aerosol
anomalies in a maritime environment.
A standoff bioaerosol sensor based on intensified range-gated spectrometric detection of Laser Induced Fluorescence
was used to spectrally characterize bioaerosol simulants during
in-chamber and open-air releases at Suffield, Canada, in
August 2008 from a standoff position. In total, 42 in-chamber Bacillus atrophaeus (formerly Bacillus subtilis var
globigii; BG) cloud and 27 open-air releases of either BG, Pantoea agglomerans (formerly Erwinia herbicola; EH),
MS2 and ovalbumin (OV) were generated. The clouds were refereed by different point sensors including Aerodynamic
Particle Sizer (APS) and slit or impingers samplers. The APS monitored the particle size distribution and concentration
and the samplers characterized the viable portion of the cloud. The extracted spectral signatures show robustness to
different degree. The correlation assessment showed good results in most cases where the LIF signal to noise ratio was
significant. The sensor 4σ sensitivity was evaluated to 1 300, 600, 100 and 30 ppl for BG, OV, MS2 and EH
respectively. Correlation results are presented by plotting the SINBAHD metric versus the corresponding particle
concentration, in which case, the obtained slope is proportional to the material fluorescence cross-section. The different
acquired signal is hence compared in terms of their fluorescence cross-section additionally to their spectral
characteristics.
We have developed a small, relatively lightweight and efficient short range (<100 m) LIDAR instrument for remotely
detecting harmful bioagents. The system is based on a pulsed, eye-safe, 355 nm laser exciting aerosols which then
fluoresce with a typical spectrum. The system makes use of a novel technology for continuously monitoring for the
presence of unusual concentrations of bioaerosols at a precise remote location within the monitored area, with response
within seconds. Fluorescence is spectrally resolved over 32 channels capable of photon counting. Results show a
sensitivity level of 40 ACPLA of Bacillus Globigii, an anthrax simulant, at a distance of 100 m (assumed worst case
where 1 ppl = 1 ACPLA) considering particle sizes between 0.5 and 10 μm, with a geometric mean at 1 um. The
apparatus has been tested in the field during three test and evaluation campaigns with multiple bioagents and public
security products. Preliminary results show that the system is able to distinguish between harmful bioagents and
naturally occurring ones. A classification algorithm was successfully tested with a single type of bioagent; experiments
for daytime measurements are discussed.
Defence R&D Canada (DRDC) has developed, by the end of the 90s, a standoff bioaerosol sensor prototype based on
intensified range-gated spectrometric detection of Laser Induced Fluorescence (LIF) called SINBAHD. This LIDAR
system was used to characterize spectrally the LIF of bioaerosol agent simulants and obscurants during 57 cross-wind
open-air releases at Suffield, CAN in July 2007. An autoclave and gamma-irradiation killing procedures were performed
on Bacillus subtilis var globigii (BG) samples before they were aerosolized, disseminated and spectrally characterized.
Slight discrepancies were observed in the spectral characteristics of killed versus live samples but none between the two
killing methodologies. Significant signature variabilities were observed from the different batches of Erwinia Herbicolas
(EH). The generated cloud was simultaneously characterized in Agent Containing Particle per Liter of Air (ACPLA) by
slit sampler units and in particle per litter of air (ppl) by an Aerodynamic Particle Sizer (APS). Correlation assessment
between the stand-off sensor SINBAHD and the two referee point sensors was done, allowing an estimation of
SINBAHD's sensitivity in ACPLA and in ppl. For a 20-m thick cloud at a range of 990 m, a detection limit of a few tens
of ACPLA and a few ACPLA were obtained for BG and EH respectively. The extracted correlation between ACPLA
and ppl data for releases performed with an agricultural sprayer showed a high degree of variability: 2 to 29% and 1 to
6% of ACPLA/ppl ratio for BG and EH, respectively.
An efficient standoff biological warfare detection capability could become an important asset for both defence and security communities based on the increasing biological threat and the limits of the presently existing protection systems. Defence R&D Canada (DRDC) has developed, by the end of the 90s, a standoff bioaerosol sensor prototype based on intensified range-gated spectrometric detection of Laser Induced Fluorescence (LIF). This LIDAR system named SINBAHD monitors the spectrally resolved LIF originating from inelastic interactions with bioaerosols present in atmospheric cells customizable in size and in range. SINBAHD has demonstrated the capability of near real-time detection and classification of bioaerosolized threats at multi-kilometre ranges. In spring 2005, DRDC has initiated the BioSense demonstration project, which combines the SINBAHD technology with a geo-referenced Near InfraRed (NIR) LIDAR cloud mapper. SINBAHD is now being used to acquire more signatures to add in the spectral library and also to optimize and test the new BioSense algorithm strategy. In September 2006, SINBAHD has participated in a two-week trial held at DRDC-Suffield where different open-air wet releases of live and killed bioagent simulants, growth media and obscurants were performed. An autoclave killing procedure was performed on two biological materials (Bacillus subtilis var globigii or BG, and Bacillus thuringiensis or Bt) before being aerosolized, disseminated and spectrally characterized with SINBAHD. The obtained results showed no significant impact of this killing process on their normalised spectral signature in comparison with their live counterparts. Correlation between the detection signals from SINBAHD, an array of slit samplers and a FLuorescent Aerosol Particle Sensor (C-FLAPS) was obtained and SINBAHD's sensitivity could then be estimated. At the 2006 trial, a detection limit of a few tens of Agent Containing Particles per Liter of Air (ACPLA) was obtained for a 15-m thick cloud of live BG located at a range of 400 m.
A compact chamber was developed for the dissemination of biological aerosols. The chamber, measuring 110 cm in length, was designed according to short-range LIDAR principles, and will be used to simulate open-air releases of aerosols. Measurements, carried out by light-induced fluorescence (LIF) techniques, will be correlated with spectroscopic data obtained with a long-range lidar system owned by Defence Research and Development Canada (DRDC). The chamber allows complete control over environmental factors, such as humidity, pressure and temperature, thus facilitating the creation of a trustworthy signature database for the standoff detection of bio-aerosols. Studies will also include the influence of growth stage, stress and growth media on the fluorescence spectra of various biological aerosols.
One of today's primary security challenges is the emerging biological threat due to the increased accessibility to
biological warfare technology and the limited efficiency of detection against such menace. At the end of the 90s, Defence
R&D Canada developed a standoff bioaerosol sensor, SINBAHD, based on intensified range-gated spectrometric
detection of Laser Induced Fluorescence (LIF) with an excitation at 351 nm. This LIDAR system generates specific
spectrally wide fluorescence signals originating from inelastic interactions with complex molecules forming the building
blocks of most bioaerosols. This LIF signal is spectrally collected by a combination of a dispersive element and a range-gated
ICCD that limits the spectral information within a selected atmospheric cell. The system can detect and classify
bioaerosols in real-time, with the help of a data exploitation process based on a least-square fit of the acquired
fluorescence signal by a linear combination of normalized spectral signatures. The detection and classification processes
are hence directly dependant on the accuracy of these signatures to represent the intrinsic fluorescence of bioaerosols and
their discrepancy. Comparisons of spectral signatures acquired at Suffield in 2001 and at Dugway in 2005 of bioaerosol
simulants, Bacillius subtilis var globiggi (BG) and Erwinia herbicola (EH), having different origin, preparation protocol
and/or dissemination modes, has been made and demonstrates the robustness of the obtained spectral signatures in these
particular cases. Specific spectral signatures and their minimum detectable concentrations for different
simulants/interferents obtained at the Joint Biological Standoff Detection System (JBSDS) increment II field
demonstration trial, Dugway Proving Ground (DPG) in June 2005, are also presented.
The biological threat has emerged as one of today's primary security challenges due to the increased accessibility to biological warfare technology and the limited efficiency of detection and protection measures against such menace. Defence Research and Development Canada (DRDC) has investigated various methods, including the improvement of atmospheric bioaerosol monitoring, to increase the readiness against such threat. By the end of the 90s, DRDC developed a standoff bioaerosol sensor based on intensified range-gated spectrometric detection of Laser Induced Fluorescence (LIF). This work has showed an important potential of detecting and discriminating in real-time several bioaerosols. The LIDAR system that monitors atmosphere cells from a standoff position induces specific spectrally wide fluorescence signals originating from inelastic interactions with complex molecules forming the building blocks of the bioaerosols. This LIF signal is spectrally collected by a combination of a dispersive element and a range-gated ICCD that records the spectral information within a range-selected atmospheric volume. To assess further the potential of discrimination of such technique, this innovative sensor was used to obtain spectral data of various natural bioaerosols. In order to evaluate the discrimination of biological agent simulants from naturally occurring background fluorescing materials, the obtained results were compared with the ones of bioaerosol simulants (Bacillius subtilis var globiggi (BG) and Erwinia herbicola (EH)) acquired in 2001. The robustness of the spectral data with time was also investigated. From our results, most of the studied natural materials showed a spectral shift of various degrees, and up to 10 nm, to the longer wavelength one year later.
Defence Research and Development Canada (DRDC) and the Canadian Space Agency (CSA) are collaborating to place a microsatellite in low earth orbit to perform optical detection and tracking of both inner-earth orbiting asteroids and earth-orbiting satellites and debris (i.e., "Resident Space Objects", RSOs). The "Near Earth Object Surveillance Satellite (NEOSSat)" will be the first mission for the CSA multi-mission microsatellite bus program, and is intended by DRDC to demonstrate the military utility of this small and inexpensive class of spacecraft. The mission will obtain metric positions, for geosynchronous satellites, to within ±500 m, timestamps accurate to within a millisecond, and be sensitive to objects in geosynchronous orbit down to 14th magnitude. The asteroid tracking mission will repeatedly survey the area from ±45-70° solar elongation with the aim of finding >50% of all inner-earth asteroids having diameters greater than 1 km.
Piezo-mounted fiber-grating output coupler is proposed as a tuning element in an Er3+-doped fiber laser. Tuning range in excess of 2 nm has been obtained in the vicinity of 1.57 micrometers . Such a tuning element is both compatible with wavelength modulation spectroscopy (WMS) and fiber laser intra-cavity spectroscopy (FLICS). CO leak detection was demonstrated using both geometries. (Delta) (alpha) of 9 X 10-4 were measured with WMS while FLICS did not lead to the expected increase of sensitivity.
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