Detection of Obscured Targets

Transcription

1 Detection of Obscured Targets Waymond R. Scott, Jr. and James Mcclellan School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta, GA

2 Outline Objectives Sensor Systems Buried Structures Buried Landmines Material Parameter Measurements Near and Far Term Goals MURI Kickoff/Progress Scott and Mcclellan, Georgia Tech 2

3 Objectives for the Georgia Tech Effort on the Obscured Targets MURI The objective of this research is to use a combination of theoretical simulation, experimental measurements, and signal processing to develop and understand innovative techniques for detecting obscured targets such as buried landmines and buried structures. MURI Kickoff/Progress Scott and Mcclellan, Georgia Tech 3

4 The Components of the Research are all Interrelated Models: Theoretical, Large scale Numerical and Experimental Signal Processing: Detection, Inversion, etc. Sensor System: Mine Detector, Buried Structure Detector, etc. Probing Signals: Electromagnetic, Seismic, Hybrid Passive/Active. Underlying Physics: Wave Interactions, Material Properties, etc. MURI Kickoff/Progress Scott and Mcclellan, Georgia Tech 4

5 Outline Objectives Sensor Systems Buried Structures Buried Landmines Material Parameter Measurements Near and Far Term Goals MURI Kickoff/Progress Scott and Mcclellan, Georgia Tech 5

6 Sensor Systems Buried Structure Detection Is it feasible to use either active or passive seismic techniques and/or electromagnetic techniques to detect buried structures? Many of the issues are similar to those for mine detection. Soil properties Seismic/Electromagnetic wave interactions Configuration Signal processing Ambient/target noise. Numerical and experimental models are also similar MURI Kickoff/Progress Scott and Mcclellan, Georgia Tech 6

7 Sensor Systems Possible configurations for a sensor to detect buried structures. These can be independent or interdependent sensors. Sensors to detect waves radiated from structure Air Soil Buried Structure Noise Source Seismic waves Radiated from Structure Electromagnetic Waves Vibration Sensing Radar Noise Source Buried Structure Structure Vibrating due to Noise Air Soil Passive Seismic Active EM to Sense Vibrations MURI Kickoff/Progress Scott and Mcclellan, Georgia Tech 7

8 Buried Structure: 2m X 3m X 3m room with 14cm thick concrete walls : Internal Source MURI Kickoff/Progress Scott and Mcclellan, Georgia Tech 8

9 Buried Structure: 2m X 3m X 3m room with 14cm thick concrete walls : External Source MURI Kickoff/Progress Scott and Mcclellan, Georgia Tech 9

10 Models Large scale numerical and experimental models will be developed for these systems. Extensions of the models developed under Demining MURI and ONR projects. Used to develop an understanding of the underlying physics. Wave interactions Material parameters Used to generate synthetics and test ideas Robust signal processing algorithms Physical theories Measurement Configurations MURI Kickoff/Progress Scott and Mcclellan, Georgia Tech 10

11 Outline Objectives Sensor Systems Buried Structures Buried Landmines Material Parameter Measurements Near and Far Term Goals MURI Kickoff/Progress Scott and Mcclellan, Georgia Tech 11

12 Sensor Systems Mine Detection Extension of Seismic/Electromagnetic Sensor developed as part of the Demining MURI and ONR projects Improve signal processing In situ characterization of the subsurface velocity profile Better mine detection algorithms Improve agreement between and experimental and numerical models Better understand/measure elastic properties of the soil Better understand/measure seismic wave interactions with mine How is the best way to configure such a system? How is the best way to sense the seismic vibrations? Can ambient seismic noise be used to detect mines? MURI Kickoff/Progress Scott and Mcclellan, Georgia Tech 12

13 Possible Configurations Elastic Wave Source Sensor S N S Rayleigh Wave Mine Displacements Air Soil MURI Kickoff/Progress Scott and Mcclellan, Georgia Tech 13

14 Possible Configurations Support Frame Seismic Source Sensor Elastic Wave MURI Kickoff/Progress Scott and Mcclellan, Georgia Tech 14

15 Photograph of the Experimental Model MURI Kickoff/Progress Scott and Mcclellan, Georgia Tech 15

16 Photograph of the Uncovered Mines and Rocks. MURI Kickoff/Progress Scott and Mcclellan, Georgia Tech 16

17 Single AT Mine Surrounded by Multiple AP Mines 30 db Scale Experimental Model Numerical Model The differences between these results are due to the inaccurate values of the material parameters used in the model. MURI Kickoff/Progress Scott and Mcclellan, Georgia Tech 17

18 Speed? One of the most significant issues that must be overcome to make a practical seismic mine detection system is measurement speed. We have been using a 4 second measurement time to maximize the signal to noise ratio in our laboratory measurements. This is overkill for a practical system Lower signal to noise ratios are adequate to find mines. A mine field will probably be much less noisy than our lab. Real soils will be more linear than the sand in the laboratory. What are reasonable measurement times? Data from an experiment at a US Government test facility Synthesize the effects of shorter measurement times. MURI Kickoff/Progress Scott and Mcclellan, Georgia Tech 18

19 US Government Test Facility VS2.2 AT mine 1 inch deep 24 cm Diameter by 11.5 cm Height Plastic MURI Kickoff/Progress Scott and Mcclellan, Georgia Tech 19

20 Surface Displacement over Mine Versus Measurement Time 4s 1s 1/4 s 1/16 s MURI Kickoff/Progress Scott and Mcclellan, Georgia Tech 20

21 Images: VS2.2 AT Mine: 1 inch deep 30 db Scale: Versus Measurement Time 4s 1s 1/4s 1/16s MURI Kickoff/Progress Scott and Mcclellan, Georgia Tech 21

22 Possible Handheld Configuration Stationary seismic source Hand scanned sensor Audible presentation of seismic waves MURI Kickoff/Progress Scott and Mcclellan, Georgia Tech 22

23 Audible Presentation The above images will be difficult to generate with a hand held mine detector. An audible presentation of the signals are easy to generate and require essentially no signal processing. The signal sensed by the radar is directly played to the operator. The incident signal can be clearly heard by the operator. This gives him confidence that the incident signal is present. The mine signal sounds hollow and is clearly distinguishable from the incident signal MURI Kickoff/Progress Scott and Mcclellan, Georgia Tech 23

24 TS-50 Mine, 3.0 cm Deep, Sandbox 60 Visual Presentation; Waterfall Graph Audible Presentation; Sound File displacement, y= time (ms) MURI Kickoff/Progress Scott and Mcclellan, Georgia Tech 24

25 Outline Objectives Sensor Systems Buried Structures Buried Landmines Material Parameter Measurements Near and Far Term Goals MURI Kickoff/Progress Scott and Mcclellan, Georgia Tech 25

26 Material Parameters Soil has very inhomogeneous and complex mechanical and electromagnetic properties These inhomogeneities and complexities are generally the limiting factor for subsurface sensing systems Techniques for measuring these properties in situ will be investigated In situ measurements are necessary because disturbing the soil significantly changes its material properties MURI Kickoff/Progress Scott and Mcclellan, Georgia Tech 26

27 Material Parameters Spectral Analysis of Surface Wave (SASW) techniques are used by geophysicist and civil engineers to make in situ measurements of the mechanical properties. However, they are generally interested in much deeper structures. We have found that the complexities of the near surface cause problems for these techniques. Modifications to existing SASW techniques and new techniques will be investigated. How should the measurement system be set up? How to calculate wave velocities? How should the data be inverted? Raleigh or Love waves? MURI Kickoff/Progress Scott and Mcclellan, Georgia Tech 27

28 Typical Surface Sensor Arrays Used in Experimental Model and at Field Test Sites Linear Array of 16 Triaxial Accelerometers Linear Array of MURI Kickoff/Progress Scott and Mcclellan, Georgia Tech 28