Abstract

A sensor constellation capable of determining the location and detailed concentration distribution of chemical warfare agent simulant clouds has been developed and demonstrated on government test ranges. The constellation is based on the use of standoff passive multispectral infrared imaging sensors to make column density measurements through the chemical cloud from two or more locations around its pe riphery. A computed tomography inversion method is employed to produce a 3D concentration profile of the cloud from the 2D line density measurements. We discuss the theoretical basis of the approach and present results of recent field experiments where controlled releases of chemical warfare agent simulants were simultaneously viewed by three chemical imaging sensors. Systematic investigations of the algorithm using synthetic data indicate that for complex functions, 3D reconstruction errors are less than 20% even in the case of a limited three-sensor measurement network. Field data results demonstrate the capability of the constellation to determine 3D concentration profiles that account for 86% of the total known mass of material released.

© 2009 Optical Society of America

Full Article  |  PDF Article

References

  • View by:
  • |
  • |
  • |

  1. D. Burnett, “Joint Program Manager NBC Contamination Avoidance,” presented at the Joint Program Executive Office for Chemical and Biological Defense (JPEO-CBD) Advanced Planning Briefing for Industry (APBI), Washington, D.C. (National Defense Industrial Association), 26 April 2005.
  2. L. B. Grim, T. C. Gruber, Jr., M. Marshall, and B. Rowland, “Chemical cloud tracking systems,” Proc. SPIE 4574, 1-6(2002).
    [CrossRef]
  3. W. J. Marinelli, K. W. Holtzclaw, S. J. Davis, and B. D. Green, “Method and apparatus for imaging,” U.S. patent 5,461,477 (24 October 1995).
  4. W. J. Marinelli, C. M. Gittins, and J. O. Jensen, “Sensor performance needs for wide area hyperspectral chemical agent detection,” presented at the Fifth Joint Conference on Standoff Detection for Chemical and Biological Defense, Williamsburg, Va. (U.S. Army), 24-28 September 2001.
  5. W. J. Marinelli, C. M. Gittins, and J. O. Jensen, “Passive multispectral imaging for standoff chemical detection,” presented at the Measurement and Chemical Intelligence (MASINT) Chemical Warfare Science and Technology Symposium, San Diego, Calif., August 2000.
  6. C. M. Gittins and W. J. Marinelli, “Remote characterization of chemical vapor plumes by LWIR imaging Fabry-Perot spectrometry,” presented at the Fifth Joint Conference on Standoff Detection for Chemical and Biological Defense, Williamsburg, Va. (U.S. Army), 24-28 September 2001.
  7. W. J. Marinelli, C. M. Gittins, A. H. Gelb, and B. D. Green, “Tunable Fabry-Pérot etalon-based long-wavelength infrared imaging spectroradiometer,” Appl. Opt. 38, 2594-2604(1999).
    [CrossRef]
  8. D. F. Flanigan, “Prediction of the limits of detection of hazardous vapors by passive infrared with the use of MODTRAN,” Appl. Opt. 35, 6090-6098 (1996).
    [CrossRef] [PubMed]
  9. B. R. Cosofret, C. M. Gittins, and W. J. Marinelli, “Visualization and tomographic analysis of chemical vapor plumes via LWIR imaging Fabry-Perot spectrometry,” Proc. SPIE 5584, 112-121 (2004).
    [CrossRef]
  10. D. Manolakis, D. Marden, and G. A. Shaw, “Hyperspectral image processing for automatic target recognition,” Lincoln Lab. J. 14, 79-115 (2003).
  11. D. Verhoeven, “Limited-data computed tomography algorithms for the physical sciences,” Appl. Opt. 32, 3736-3754(1993).
    [CrossRef] [PubMed]
  12. R. E. Snyder, R. G. Joklik, and H. G. Smerjian, “Laser tomographic measurement in an unsteady jet diffusion flame,” in Annual Meeting of American Society of Mechanical Engineers (American Society of Mechanical Engineers, 1989), pp. 10-15.
  13. M. Hino, T. Aono, M. Nakajima, and S. Yuta, “Light emission computed tomography system for plasma diagnostics,” Appl. Opt. 26, 4742-4746 (1987).
    [CrossRef] [PubMed]
  14. D. D. Verhoeven, “An experimental study of the performance of an optical tomography system,” in Proc. SPIE 1162, 369-377 (1990).
  15. R. N. Bracewell, “Strip integration in radio astronomy,” Aust. J. Phys. 9, 198-217 (1956).
    [CrossRef]
  16. J. Feng, “Reconstruction in tomography from severe incomplete projection data using multiresolution analysis and optimization,” in IEEE Digital Signal Processing Workshop Proceedings, 1996 (IEEE, 1996), pp. 133-136.
    [CrossRef]
  17. L. A. Todd and R. Bhattacharyya, “Tomographic reconstruction of air pollutants: evaluation of measurement geometries,” Appl. Opt. 36, 7678-7688 (1997).
    [CrossRef]

2004 (1)

B. R. Cosofret, C. M. Gittins, and W. J. Marinelli, “Visualization and tomographic analysis of chemical vapor plumes via LWIR imaging Fabry-Perot spectrometry,” Proc. SPIE 5584, 112-121 (2004).
[CrossRef]

2003 (1)

D. Manolakis, D. Marden, and G. A. Shaw, “Hyperspectral image processing for automatic target recognition,” Lincoln Lab. J. 14, 79-115 (2003).

2002 (1)

L. B. Grim, T. C. Gruber, Jr., M. Marshall, and B. Rowland, “Chemical cloud tracking systems,” Proc. SPIE 4574, 1-6(2002).
[CrossRef]

1999 (1)

1997 (1)

1996 (2)

D. F. Flanigan, “Prediction of the limits of detection of hazardous vapors by passive infrared with the use of MODTRAN,” Appl. Opt. 35, 6090-6098 (1996).
[CrossRef] [PubMed]

J. Feng, “Reconstruction in tomography from severe incomplete projection data using multiresolution analysis and optimization,” in IEEE Digital Signal Processing Workshop Proceedings, 1996 (IEEE, 1996), pp. 133-136.
[CrossRef]

1993 (1)

1990 (1)

D. D. Verhoeven, “An experimental study of the performance of an optical tomography system,” in Proc. SPIE 1162, 369-377 (1990).

1989 (1)

R. E. Snyder, R. G. Joklik, and H. G. Smerjian, “Laser tomographic measurement in an unsteady jet diffusion flame,” in Annual Meeting of American Society of Mechanical Engineers (American Society of Mechanical Engineers, 1989), pp. 10-15.

1987 (1)

1956 (1)

R. N. Bracewell, “Strip integration in radio astronomy,” Aust. J. Phys. 9, 198-217 (1956).
[CrossRef]

Aono, T.

Bhattacharyya, R.

Bracewell, R. N.

R. N. Bracewell, “Strip integration in radio astronomy,” Aust. J. Phys. 9, 198-217 (1956).
[CrossRef]

Burnett, D.

D. Burnett, “Joint Program Manager NBC Contamination Avoidance,” presented at the Joint Program Executive Office for Chemical and Biological Defense (JPEO-CBD) Advanced Planning Briefing for Industry (APBI), Washington, D.C. (National Defense Industrial Association), 26 April 2005.

Cosofret, B. R.

B. R. Cosofret, C. M. Gittins, and W. J. Marinelli, “Visualization and tomographic analysis of chemical vapor plumes via LWIR imaging Fabry-Perot spectrometry,” Proc. SPIE 5584, 112-121 (2004).
[CrossRef]

Davis, S. J.

W. J. Marinelli, K. W. Holtzclaw, S. J. Davis, and B. D. Green, “Method and apparatus for imaging,” U.S. patent 5,461,477 (24 October 1995).

Feng, J.

J. Feng, “Reconstruction in tomography from severe incomplete projection data using multiresolution analysis and optimization,” in IEEE Digital Signal Processing Workshop Proceedings, 1996 (IEEE, 1996), pp. 133-136.
[CrossRef]

Flanigan, D. F.

Gelb, A. H.

Gittins, C. M.

W. J. Marinelli, C. M. Gittins, and J. O. Jensen, “Passive multispectral imaging for standoff chemical detection,” presented at the Measurement and Chemical Intelligence (MASINT) Chemical Warfare Science and Technology Symposium, San Diego, Calif., August 2000.

C. M. Gittins and W. J. Marinelli, “Remote characterization of chemical vapor plumes by LWIR imaging Fabry-Perot spectrometry,” presented at the Fifth Joint Conference on Standoff Detection for Chemical and Biological Defense, Williamsburg, Va. (U.S. Army), 24-28 September 2001.

W. J. Marinelli, C. M. Gittins, and J. O. Jensen, “Sensor performance needs for wide area hyperspectral chemical agent detection,” presented at the Fifth Joint Conference on Standoff Detection for Chemical and Biological Defense, Williamsburg, Va. (U.S. Army), 24-28 September 2001.

B. R. Cosofret, C. M. Gittins, and W. J. Marinelli, “Visualization and tomographic analysis of chemical vapor plumes via LWIR imaging Fabry-Perot spectrometry,” Proc. SPIE 5584, 112-121 (2004).
[CrossRef]

W. J. Marinelli, C. M. Gittins, A. H. Gelb, and B. D. Green, “Tunable Fabry-Pérot etalon-based long-wavelength infrared imaging spectroradiometer,” Appl. Opt. 38, 2594-2604(1999).
[CrossRef]

Green, B. D.

W. J. Marinelli, K. W. Holtzclaw, S. J. Davis, and B. D. Green, “Method and apparatus for imaging,” U.S. patent 5,461,477 (24 October 1995).

W. J. Marinelli, C. M. Gittins, A. H. Gelb, and B. D. Green, “Tunable Fabry-Pérot etalon-based long-wavelength infrared imaging spectroradiometer,” Appl. Opt. 38, 2594-2604(1999).
[CrossRef]

Grim, L. B.

L. B. Grim, T. C. Gruber, Jr., M. Marshall, and B. Rowland, “Chemical cloud tracking systems,” Proc. SPIE 4574, 1-6(2002).
[CrossRef]

Gruber, T. C.

L. B. Grim, T. C. Gruber, Jr., M. Marshall, and B. Rowland, “Chemical cloud tracking systems,” Proc. SPIE 4574, 1-6(2002).
[CrossRef]

Hino, M.

Holtzclaw, K. W.

W. J. Marinelli, K. W. Holtzclaw, S. J. Davis, and B. D. Green, “Method and apparatus for imaging,” U.S. patent 5,461,477 (24 October 1995).

Jensen, J. O.

W. J. Marinelli, C. M. Gittins, and J. O. Jensen, “Passive multispectral imaging for standoff chemical detection,” presented at the Measurement and Chemical Intelligence (MASINT) Chemical Warfare Science and Technology Symposium, San Diego, Calif., August 2000.

W. J. Marinelli, C. M. Gittins, and J. O. Jensen, “Sensor performance needs for wide area hyperspectral chemical agent detection,” presented at the Fifth Joint Conference on Standoff Detection for Chemical and Biological Defense, Williamsburg, Va. (U.S. Army), 24-28 September 2001.

Joklik, R. G.

R. E. Snyder, R. G. Joklik, and H. G. Smerjian, “Laser tomographic measurement in an unsteady jet diffusion flame,” in Annual Meeting of American Society of Mechanical Engineers (American Society of Mechanical Engineers, 1989), pp. 10-15.

Manolakis, D.

D. Manolakis, D. Marden, and G. A. Shaw, “Hyperspectral image processing for automatic target recognition,” Lincoln Lab. J. 14, 79-115 (2003).

Marden, D.

D. Manolakis, D. Marden, and G. A. Shaw, “Hyperspectral image processing for automatic target recognition,” Lincoln Lab. J. 14, 79-115 (2003).

Marinelli, W. J.

W. J. Marinelli, C. M. Gittins, and J. O. Jensen, “Passive multispectral imaging for standoff chemical detection,” presented at the Measurement and Chemical Intelligence (MASINT) Chemical Warfare Science and Technology Symposium, San Diego, Calif., August 2000.

W. J. Marinelli, C. M. Gittins, and J. O. Jensen, “Sensor performance needs for wide area hyperspectral chemical agent detection,” presented at the Fifth Joint Conference on Standoff Detection for Chemical and Biological Defense, Williamsburg, Va. (U.S. Army), 24-28 September 2001.

C. M. Gittins and W. J. Marinelli, “Remote characterization of chemical vapor plumes by LWIR imaging Fabry-Perot spectrometry,” presented at the Fifth Joint Conference on Standoff Detection for Chemical and Biological Defense, Williamsburg, Va. (U.S. Army), 24-28 September 2001.

W. J. Marinelli, K. W. Holtzclaw, S. J. Davis, and B. D. Green, “Method and apparatus for imaging,” U.S. patent 5,461,477 (24 October 1995).

B. R. Cosofret, C. M. Gittins, and W. J. Marinelli, “Visualization and tomographic analysis of chemical vapor plumes via LWIR imaging Fabry-Perot spectrometry,” Proc. SPIE 5584, 112-121 (2004).
[CrossRef]

W. J. Marinelli, C. M. Gittins, A. H. Gelb, and B. D. Green, “Tunable Fabry-Pérot etalon-based long-wavelength infrared imaging spectroradiometer,” Appl. Opt. 38, 2594-2604(1999).
[CrossRef]

Marshall, M.

L. B. Grim, T. C. Gruber, Jr., M. Marshall, and B. Rowland, “Chemical cloud tracking systems,” Proc. SPIE 4574, 1-6(2002).
[CrossRef]

Nakajima, M.

Rowland, B.

L. B. Grim, T. C. Gruber, Jr., M. Marshall, and B. Rowland, “Chemical cloud tracking systems,” Proc. SPIE 4574, 1-6(2002).
[CrossRef]

Shaw, G. A.

D. Manolakis, D. Marden, and G. A. Shaw, “Hyperspectral image processing for automatic target recognition,” Lincoln Lab. J. 14, 79-115 (2003).

Smerjian, H. G.

R. E. Snyder, R. G. Joklik, and H. G. Smerjian, “Laser tomographic measurement in an unsteady jet diffusion flame,” in Annual Meeting of American Society of Mechanical Engineers (American Society of Mechanical Engineers, 1989), pp. 10-15.

Snyder, R. E.

R. E. Snyder, R. G. Joklik, and H. G. Smerjian, “Laser tomographic measurement in an unsteady jet diffusion flame,” in Annual Meeting of American Society of Mechanical Engineers (American Society of Mechanical Engineers, 1989), pp. 10-15.

Todd, L. A.

Verhoeven, D.

Verhoeven, D. D.

D. D. Verhoeven, “An experimental study of the performance of an optical tomography system,” in Proc. SPIE 1162, 369-377 (1990).

Yuta, S.

Appl. Opt. (5)

Aust. J. Phys. (1)

R. N. Bracewell, “Strip integration in radio astronomy,” Aust. J. Phys. 9, 198-217 (1956).
[CrossRef]

Lincoln Lab. J. (1)

D. Manolakis, D. Marden, and G. A. Shaw, “Hyperspectral image processing for automatic target recognition,” Lincoln Lab. J. 14, 79-115 (2003).

Proc. SPIE (3)

B. R. Cosofret, C. M. Gittins, and W. J. Marinelli, “Visualization and tomographic analysis of chemical vapor plumes via LWIR imaging Fabry-Perot spectrometry,” Proc. SPIE 5584, 112-121 (2004).
[CrossRef]

L. B. Grim, T. C. Gruber, Jr., M. Marshall, and B. Rowland, “Chemical cloud tracking systems,” Proc. SPIE 4574, 1-6(2002).
[CrossRef]

D. D. Verhoeven, “An experimental study of the performance of an optical tomography system,” in Proc. SPIE 1162, 369-377 (1990).

Other (7)

R. E. Snyder, R. G. Joklik, and H. G. Smerjian, “Laser tomographic measurement in an unsteady jet diffusion flame,” in Annual Meeting of American Society of Mechanical Engineers (American Society of Mechanical Engineers, 1989), pp. 10-15.

J. Feng, “Reconstruction in tomography from severe incomplete projection data using multiresolution analysis and optimization,” in IEEE Digital Signal Processing Workshop Proceedings, 1996 (IEEE, 1996), pp. 133-136.
[CrossRef]

W. J. Marinelli, K. W. Holtzclaw, S. J. Davis, and B. D. Green, “Method and apparatus for imaging,” U.S. patent 5,461,477 (24 October 1995).

W. J. Marinelli, C. M. Gittins, and J. O. Jensen, “Sensor performance needs for wide area hyperspectral chemical agent detection,” presented at the Fifth Joint Conference on Standoff Detection for Chemical and Biological Defense, Williamsburg, Va. (U.S. Army), 24-28 September 2001.

W. J. Marinelli, C. M. Gittins, and J. O. Jensen, “Passive multispectral imaging for standoff chemical detection,” presented at the Measurement and Chemical Intelligence (MASINT) Chemical Warfare Science and Technology Symposium, San Diego, Calif., August 2000.

C. M. Gittins and W. J. Marinelli, “Remote characterization of chemical vapor plumes by LWIR imaging Fabry-Perot spectrometry,” presented at the Fifth Joint Conference on Standoff Detection for Chemical and Biological Defense, Williamsburg, Va. (U.S. Army), 24-28 September 2001.

D. Burnett, “Joint Program Manager NBC Contamination Avoidance,” presented at the Joint Program Executive Office for Chemical and Biological Defense (JPEO-CBD) Advanced Planning Briefing for Industry (APBI), Washington, D.C. (National Defense Industrial Association), 26 April 2005.

Cited By

OSA participates in CrossRef's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (18)

Fig. 1
Fig. 1

Imaging sensor constellation for tomographic chemical cloud mapping.

Fig. 2
Fig. 2

Technical concept for Fabry–Perot imaging spectrometer. The interferometer is located in an afocal region of the optical train.

Fig. 3
Fig. 3

Schematic diagram of three-layer radiative transfer model.

Fig. 4
Fig. 4

Chemical simulant identification in laboratory absorption cell.

Fig. 5
Fig. 5

ACE-estimated column density versus mass-flow-derived column density.

Fig. 6
Fig. 6

Error contribution to the sensor radiometric measurement; 1 μFlux / μFlick = 1 μW / ( cm 2 sr μm ) .

Fig. 7
Fig. 7

Graphical representation of the reconstruction technique.

Fig. 8
Fig. 8

Test Functions for validating the reconstruction algorithm. A, 2D cosine function. B, 2D cosine function as basis with one additional peak. C, 2D cosine function as basis with two additional peaks. D, combination of multiple 2D Gaussians with random spatial distribution providing a propagation axis that is not normal to either of the primary axes of the grid.

Fig. 9
Fig. 9

Reconstruction results using 2 views (0°, 90°) and 128 projections per view: A, function A; B, function B; C, function C; D, function D.

Fig. 10
Fig. 10

Reconstruction of function D with three sensor views (0°, 45°, 90°).

Fig. 11
Fig. 11

Reconstruction of function D with four sensor views (0°, 45°, 90°, 135°).

Fig. 12
Fig. 12

Simultaneous detection of (top) AA and (bottom) TEP from all three AIRIS-WAD sensors.

Fig. 13
Fig. 13

Number of detected pixels for each of the sensors as a function of time after initial burst: top,  120 kg AA release; bottom,  90 kg TEP release.

Fig. 14
Fig. 14

AA concentration distribution at 0 12 m elevation above ground: contour map (left) and surface plot (right).

Fig. 15
Fig. 15

AA concentration distribution at 12 24 m elevation above ground: contour map (left) and surface plot (right).

Fig. 16
Fig. 16

AA concentration distributions at 0 12 m (left) and 12 24 m (right) above ground.

Fig. 17
Fig. 17

Calculated total mass in the 3D volume as a function of time after the initial release of 120 kg of AA.

Fig. 18
Fig. 18

Mass estimation based on the 3D tomographic reconstruction of chemical clouds for several runs (mass estimation expressed as percent of the total known mass released on the test grid).

Equations (23)

Equations on this page are rendered with MathJax. Learn more.

N sensor ( λ ) = t A t C N B ( λ , T 1 ) ε B ( λ ) + t A [ 1 t C ] N C ( λ , T 2 ) + [ 1 t A ] N A ( λ , T 3 ) ,
t C ( λ ) = exp [ σ i ( λ ) ρ i ] , ρ i = C i ,
Δ N ( λ ) = [ t A t C N B ( λ , T 1 ) ε B ( λ ) + t A [ 1 t C ] N C ( λ , T 2 ) + [ 1 t A ] N A ( λ , T 3 ) ] N ^ ( λ ) ,
N ^ ( λ ) = t A N B ( λ , T 1 ) ε B ( λ ) + [ 1 t A ] N A ( λ , T 3 ) .
t C ( λ ) = N sensor ( λ ) N A ( λ ) N ^ ( λ ) N A ( λ ) ,
x = μ + S α + ν
z ̲ = Γ̳ 1 / 2 ( x μ ) = Γ̳ 1 / 2 S α + n ,
D ACE ( z ̲ ) = z ̲ T ( Γ̳ 1 / 2 S ) ( S T Γ̳ 1 S ) 1 ( Γ̳ 1 / 2 S ) T z ̲ z ̲ T z ̲ .
α ^ = ( S T Γ̳ 1 S ) 1 ( S ) T Γ̳ 1 ( x μ ) .
N ^ ( λ ) N sensor ( λ ) S α ^ ,
ρ ^ i = α ^ i L / T N A ( N sensor S α ^ ) ,
σ ρ 2 = ( ρ α ) 2 σ α 2 + ( ρ N sensor ) 2 σ N sensor 2 + ( ρ N A ) 2 ( N A T A ) 2 σ T A 2 ,
ψ p ( x , z ) 0 L C ( x , y , z ) d y ,
ψ p = j M N Q O j s b p ( x x j , y y j , z z j ) d s ,
[ ψ p ] = [ W i j ] [ O j ] .
P ( O j ) = e O j ¯ O j ¯ O j O j ! .
ψ ˜ p = j M N Q w p j O j .
P ( ψ p ) = p = 1 P e ψ ˜ p ψ ˜ p ψ p ψ p ! .
ln ( P ( ψ p ) ) = p = 1 P ( ψ ˜ p ) + p = 1 P ln ( ψ ˜ p ψ p ψ p ! ) = p = 1 P ( ψ ˜ p ) + p = 1 P ψ p ln ( ψ ˜ p ) p = 1 P ln ( ψ p ) = p = 1 P j M N Q w p j O j + p = 1 P ψ p ln ( j M N Q w p j O j ) p = 1 P ln ( ψ p ) .
O j [ ln ( P ( ψ p ) ] = p = 1 P j = 1 M N Q w p j + p = 1 P ψ p j = 1 M N Q O j = 0.
O j n + 1 = p = 1 P ψ p O j w p j j = 1 M N Q w p j O j = O j n p = 1 P ψ p w p j j = 1 M N Q w p j O j = O j n p = 1 P w p j [ ψ p / ψ ˜ p ] j w p j .
T n = p = 1 P w p j [ ψ p / ψ ˜ p ] j w p j ;
0 < nearness = j x y ( O j * - O j ) 2 j x y ( O j * - O avg * ) 2 1 ,

Metrics