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Outline

   Standoff detection of body-worn explosives
   General detection approach
   FDFD Modeling
   Experiment description
   Signal processing
   Results
Detection Approach




 Narrowbeam, 4.3 cm wide region of
  illumination
Narrow beamwidth

 Projected scanned beam on target:




                                      Images courtesy of
                                      Borja Gonzalez-
                                      Valdes, Northeastern
                                      University
FDFD Modeling

 2-Dimensional TMz Polarization
 Multistatic and multi-monostatic
  configurations simulated
 Multiple frequencies simulated for future
  frequency-modulated continuous-wave
  (FMCW) simulations
FDFD Modeling (cont…)
 Geometry
  files used
FDFD simulation parameters
 13 uniform plane
  waves (UPW)
 Range = 10 m
 2.7o aperture (about
  0.47 m @ 10m
  Range)
Focused beam
 Superposition of 13 UPW with phase adjusted
  according to position of focus.
Received fields

 3D Near field to near field transformation
 Received field function of scanned beam
  position
   Multistatic (multiple transmitters multiple
    receivers)
   Multi-monostatic (single transmitter, single
    receiver)
Received fields (aperture = 0.47 meters)
Received fields (aperture = 0.8 meters)
Better cross range resolution w/ larger aperture


0.47 m                          0.8 m
Experimental Description
                         Radar Module




                          Feed Antenna




                            Reflector


Tuning Voltage
  Hardware
FMCW signal processing

 Frequency-modulated continuous-wave radar
                 Range Profile of Corner Cube


          18000 range profiles
           obtained as target
           rotated 360 degrees on
           turn table
ISAR processing
Targets used for measurements

 Skin reflects 70% as well as metal at 94 GHz
  due to high water content.
 Scattering dominated by natural unknown
  contours of human body.
 Must use very diverse body types to test
  robustness of system.
Signal Processing

 Standoff detection constraint restricts total
  azimuthal view of target.
 Must use finite azimuth segments of range
  profiles to simulate limited view of an actual
  radar system.




                         FFT
Example limited view ISAR images (2.7o aperture)




               Extended early
                and late time
                 response.
Target classification

 Best fit ellipse finding algorithm used for
  quantifying elongated time response



                     a

               b



        a/b > Threshold          a/b < Threshold
           no threat                threat
Results
Conclusion
 Multiple features in limited view ISAR images
  distinguishing threats from non-threats.
 Largest distinguishing feature is the elongated
  time response.
 Ellipse finding algorithm has been successful in
  distinguishing threats from non threats.
 Limited aperture size and phase noise limit
  resolution and capabilities of system.
 Multi-static system with digitally controlled
  frequency ramp could lead to a commercial
  detection system.
References
Acknowledgements
Questions?

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J Fernandes Hst 2009

  • 1.
  • 2. Outline  Standoff detection of body-worn explosives  General detection approach  FDFD Modeling  Experiment description  Signal processing  Results
  • 3. Detection Approach  Narrowbeam, 4.3 cm wide region of illumination
  • 4. Narrow beamwidth  Projected scanned beam on target: Images courtesy of Borja Gonzalez- Valdes, Northeastern University
  • 5. FDFD Modeling  2-Dimensional TMz Polarization  Multistatic and multi-monostatic configurations simulated  Multiple frequencies simulated for future frequency-modulated continuous-wave (FMCW) simulations
  • 6. FDFD Modeling (cont…)  Geometry files used
  • 7. FDFD simulation parameters  13 uniform plane waves (UPW)  Range = 10 m  2.7o aperture (about 0.47 m @ 10m Range)
  • 8. Focused beam  Superposition of 13 UPW with phase adjusted according to position of focus.
  • 9. Received fields  3D Near field to near field transformation  Received field function of scanned beam position  Multistatic (multiple transmitters multiple receivers)  Multi-monostatic (single transmitter, single receiver)
  • 10. Received fields (aperture = 0.47 meters)
  • 11. Received fields (aperture = 0.8 meters)
  • 12. Better cross range resolution w/ larger aperture 0.47 m 0.8 m
  • 13. Experimental Description Radar Module Feed Antenna Reflector Tuning Voltage Hardware
  • 14. FMCW signal processing  Frequency-modulated continuous-wave radar Range Profile of Corner Cube  18000 range profiles obtained as target rotated 360 degrees on turn table
  • 16. Targets used for measurements  Skin reflects 70% as well as metal at 94 GHz due to high water content.  Scattering dominated by natural unknown contours of human body.  Must use very diverse body types to test robustness of system.
  • 17. Signal Processing  Standoff detection constraint restricts total azimuthal view of target.  Must use finite azimuth segments of range profiles to simulate limited view of an actual radar system. FFT
  • 18. Example limited view ISAR images (2.7o aperture) Extended early and late time response.
  • 19. Target classification  Best fit ellipse finding algorithm used for quantifying elongated time response a b a/b > Threshold a/b < Threshold  no threat  threat
  • 21. Conclusion  Multiple features in limited view ISAR images distinguishing threats from non-threats.  Largest distinguishing feature is the elongated time response.  Ellipse finding algorithm has been successful in distinguishing threats from non threats.  Limited aperture size and phase noise limit resolution and capabilities of system.  Multi-static system with digitally controlled frequency ramp could lead to a commercial detection system.