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Design and Optimization of Passive and Active Imaging Radar

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1 Design and Optimization of Passive and Active Imaging Radar
DARPA grant F Dept. of Electrical and Computer Engineering in collaboration with Gaithersburg, MD Sponsored by Administered by

2 Objectives Apply statistical inference techniques, information theory, and state-of-the-art physics-based modeling of electromagnetic phenomena to develop algorithms for imaging and recognizing airborne targets via radar. Emphasize passive systems which exploit “illuminators of opportunity” such as commercial TV and FM radio broadcasts Predict the fundamental performance limits of any system employing this kind of data

3 The Team Faculty Graduate Students Postdocs Pierre Moulin YoramBresler
Dave Munson Chew Weng Faculty Dick Blahut Yong Wu Shawn Herman Raman Venkataramani Graduate Students Shu Xiao SoumyaJana Michael Brandfass Jong Ye Postdocs Lanterman Aaron

4 Passive Radar Systems Multistatic system using commercial transmitters
System remains covert No cost of building transmitters Coverage of low altitude targets Television and FM radio signals Low frequency Low practical bandwidths On all the time Good doppler resolution, poor range resolution Need high SNR receivers

5 Interaction with Lockheed Martin
The Passive Coherent Location (PCL) group at Lockheed Martin Mission Systems in Gaithersburg, MD is acting as an unfunded and unfunding partner Makers of the Silent SentryTM PCL system Helped isolate specific areas of investigation Provided Silent SentryTM data (position, velocity, complex reflectances) of a cooperatively flown Dassault Falcon 20 observed using 3 FM transmitters

6 Our Vision: 3-D Target Tracking Positions Velocities Complex
Reflectances Linear Imaging (Tomographic ISAR/ Time-Frequency Analysis) Silent SentryTM 3-D Enhanced Tracking via Classification and Orientation Estimation Nonlinear Imaging (Physics-Based Inverse Scattering) FISC (Signature Prediction) DEMACO/SAIC Champaign Target Classification Target Library

7 FISC Databases 0 deg. elevation, HH polarization Falcon 100 VFY-218
Shawn Herman 0 deg. elevation, HH polarization Falcon 100 VFY-218 Stealth Fighter

8 Classification via FISC Databases
Pierre Moulin Classification via FISC Databases Shawn Herman Three transmitters, one receiver, three-class problem

9 Classification via FISC Databases
Pierre Moulin Classification via FISC Databases Shawn Herman Three transmitters, one receiver, three-class problem

10 Large-Aperture Tomographic Radar
Yong Wu Dave Munson Large-Aperture Tomographic Radar MHz (TV Channels 2 - 6) VFY-218 Stealth Fighter HH polariz. VV polariz. HV polariz.

11 Small-Aperture Tomographic Radar
Yong Wu Dave Munson Small-Aperture Tomographic Radar MHz (TV Channels 2 - 6), HH polarization VFY-218 Stealth Fighter Tail-on Broadside Nose-on

12 2-D Comparison of Fast Reconstruction Techniques
Michael Brandfass 2-D Comparison of Fast Reconstruction Techniques k=7, 64 incident angles, 64 observation angles Diffraction Tomography (Born Approx.) Truth Colton/Kirsch “Linear sampling” TM polarization TE polarization

13 2-D Comparison of Fast Reconstruction Techniques
Michael Brandfass 2-D Comparison of Fast Reconstruction Techniques k=7, TM polarization, 64 observation angles Diffraction Tomography (Born Approx.) Colton/Kirsch “Linear sampling” 64 incident 32 incident 16 incident

14 Distorted Born Iterative Method: Airplane Model
Michael Brandfass Chew Weng Distorted Born Iterative Method: Airplane Model k=1.5 to 9.2, TE polarization, 64 inc. angles, 250 obs. angles

15 Distorted Born Iterative Method: Circle Model
Michael Brandfass Chew Weng Distorted Born Iterative Method: Circle Model k=7, TE polarization, 32 incident angles, 32 observation angles Colton/Kirsch (for comparison)

16 Fast Multilevel Backprojection Algorithm
Shu Xiao Dave Munson Fast Multilevel Backprojection Algorithm Traditional backprojection algorithm: O(N3) computation New backprojection algorithm: O(N log N) computation Inspired by the multilevel fast multipole algorithms (MLFMA) of computational electromagnetics New algorithm can readily accommodate curved projections for near-field imaging (for instance, imaging runways)

17 To Learn More... Technical POC: Dr. Aaron Lanterman work: 217-333-9638
home: Project website:

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