Long-wave infrared multi-wavelength IPDA lidar for standoff detection of chemical warfare agents: theoretical study

2020 ◽  
Vol 59 (35) ◽  
pp. 11167 ◽  
Author(s):  
Nicolas Cézard ◽  
Jean-Michel Melkonian
2020 ◽  
Vol 59 (35) ◽  
pp. 11156 ◽  
Author(s):  
Jean-Michel Melkonian ◽  
Julie Armougom ◽  
Myriam Raybaut ◽  
Jean-Baptiste Dherbecourt ◽  
Guillaume Gorju ◽  
...  

2011 ◽  
Author(s):  
Scott E. Bisson ◽  
Jeffrey M. Headrick ◽  
Thomas A. Reichardt ◽  
Roger L. Farrow ◽  
Thomas J. Kulp

2008 ◽  
Vol 18 (02) ◽  
pp. 457-468
Author(s):  
HUGO LAVOIE ◽  
ELDON PUCKRIN ◽  
JEAN-MARC THÉRIAULT

In this paper, the passive standoff long wave infrared technology developed for atmospheric remote sensing was used to detect and identify chemical pollutants in the atmosphere. The measurement approach is based on the differential passive standoff detection method that has been developed by DRDC Valcartier during the past few years. The measurements were performed on real chemical warfare agents and toxic chemical vapors. The results clearly demonstrate the capability of the differential radiometry approach for the detection, identification and quantification of toxic chemical vapor clouds in an open-air environment.


Author(s):  
Chang Sik Lee ◽  
Hyeong-Geun Yu ◽  
Jae-Hyeon Park ◽  
Whimin Kim ◽  
Dong-Jo Park ◽  
...  

Raman spectrometers are studied and developed for the military purposes because of their nondestructive inspection capability to capture unique spectral features induced by molecular structures of colorless and odorless chemical warfare agents(CWAs) in any phase. Raman spectrometers often suffer from random noise caused by their detector inherent noise, background signal, etc. Thus, reducing the random noise in a measured Raman spectrum can help detection algorithms to find spectral features of CWAs and effectively detect them. In this paper, we propose a denoising autoencoder for Raman spectra with a loss function for sample efficient learning using noisy dataset. We conduct experiments to compare its effect on the measured spectra and detection performance with several existing noise reduction algorithms. The experimental results show that the denoising autoencoder is the most effective noise reduction algorithm among existing noise reduction algorithms for Raman spectrum based standoff detection of CWAs.


2017 ◽  
Vol 72 (1) ◽  
pp. 151-158 ◽  
Author(s):  
Guangxiao Hu ◽  
Wei Xiong ◽  
Haiyan Luo ◽  
Hailiang Shi ◽  
Zhiwei Li ◽  
...  

Raman spectroscopic detection is one of the suitable methods for the detection of chemical warfare agents (CWAs) and simulants. Since the 1980s, many researchers have been dedicated to the research of chemical characteristic of CWAs and simulants and instrumental improvement for their analysis and detection. The spatial heterodyne Raman spectrometer (SHRS) is a new developing instrument for Raman detection that appeared in 2011. It is already well-known that SHRS has the characteristics of high spectral resolution, a large field-of-view, and high throughput. Thus, it is inherently suitable for the analysis and detection of these toxic chemicals and simulants. The in situ and standoff detection of some typical simulants of CWAs, such as dimethyl methylphosphonate (DMMP), diisopropyl methylphosphonate (DIMP), triethylphosphate (TEP), diethyl malonate (DEM), methyl salicylate (MES), 2-chloroethyl ethyl sulfide (CEES), and malathion, were tried. The achieved results show that SHRS does have the ability of in situ analysis or standoff detection for simulants of CWAs. When the laser power was set to as low as 26 mW, the SHRS still has a signal-to-noise ratio higher than 5 in in situ detection. The standoff Raman spectra detection of CWAs simulants was realized at a distance of 11 m. The potential feasibility of standoff detection of SHRS for CWAs simulants has been proved.


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