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
The two primary thrusts detailed on this poster are: recognition and imaging

3. The Team * Denotes alumnus Faculty Graduate Students * Postdocs * * *
Pierre Moulin
YoramBresler
Dave Munson
Chew
Weng
Faculty
Dick
Blahut
Yong Wu
Shawn Herman
Raman Venkataramani
Jeffrey Brokish
Capt. Larkin Hastriter
Graduate
Students
Shu
Xiao
Alaeddin Aydiner *
SoumyaJana
Rob Morrison
Schmid
Natalia
Michael Brandfass
Jong Ye
Postdocs *
Warnick
Karl * *
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: Target Tracking Positions Velocities Complex Reflectances
Linear Imaging
(Tomographic ISAR/
Time-Frequency Analysis)
PCL System
Enhanced Tracking via Classification and Orientation Estimation (future work)
Nonlinear Imaging
(Physics-Based
Inverse Scattering)
FISC (Signature Prediction)
DEMACO/SAIC Champaign
Target
Classification
Target Library

7. Example CAD Models
F-22
Falcon-100
Flying Bat
VFY-218

8. FISC Databases
Calculate/store RCS at each incident and observed aspect
Use Fast Illinois Solver Code (FISC), which is more accurate than XPATCH for wavelengths of interest
Shown for 0 Deg. El.,
HH polarization
Falcon 100
VFY-218

9. Target Recognition via FISC Databases
Pierre Moulin
Target Recognition via FISC Databases
Shawn Herman
Compare collected data to simulated data via a statistical loglikelihood function
Just want recognition, so target position and orientation are nuisance parameters
Search simplified via Markov assumption on nuisance parameters
Big advantage to using TV and FM radio:
need to store fewer RCS values at lower frequencies!

10. Data Collection Scenario in Gaithersburg, MD Area

11. FISC Predictions of RCS Data for Different Targets
Graph for
straight portion of blue flight path
and transmitter
in upper-right
hand corner of
previous slide

12. Markov Assumption Simplifies Handling of Nuisance Parameters
HMM state matrix
j+1
orientation j
position

13. Monte Carlo Recognition Experiment: Problem Setup
TRG330 from RedBlue data (N=428)
ground truth via DGPS used to simulate “collected” data
used Silent Sentry position estimates to classify target
synthesize raw data with additive complex white Gaussian noise
3 FM illuminators (WFRE,WFSI,WXYV)
forced zero elevation (3-D state vector)
noncoherent classification (RCS only)
5 targets
1000 simulations per target

14. Monte Carlo Recognition Experiment: Confusion Matrix Results
Recognized Target
779 80
131 10
F-22 52
834 24
Falcon-100 1 68
931
Falcon-20
104 21
872 3
Flying Bat 7 14 13
965
VFY-218
Correct Target

15. Recognition Experiment Using Real Collected Data
Similar to Monte Carlo experimental setup, except now classifying real data collected by Lockheed Martin
True target is a Falcon-20
4 confusing targets
Falcon-20 was correctly identified in real data using
Silent Sentry position estimates
Silent Sentry velocity estimates to guess orientation
Improvements must be made to get “real” and “simulated” data to match more closely to enable identification using data collected over smaller time frames

16. Tomographic Imaging from Bistatic Radar Data
Yong Wu
Dave Munson
Collected complex data gives samples of the Fourier transform of the target’s reflectance
Angular Fourier coordinates determined by direction of bisecting vector
Radial coordinate of points in Fourier space determined by transmitted frequency  cos(bistatic angle/2)
Coverage in Fourier space from frequency and angular diversity
System Geometry

17. Geometry for Imaging Experiment
Sampling pattern in Fourier space resulting from geometry with receiver location shown in left figure

18. Sampling Patterns for Different Receiver Locations

19. Tomographic Images for Different Receiver Locations
Using 21 TV transmitters and FISC data for Falcon 20
Columns 1 and 3: Interpolated grids in Fourier space
Columns 2 and 4: IFFTs of interpolated Fourier grids

20. Tomographic Images for Different Numbers of Transmitters
Using 13
TV transmitters
Using 9
TV transmitters

21. Point Spread Functions for Different Receiver Locations
For imaging, we would like to pick receiver location to give a good PSF (narrow main beam, low sidelobes)

22. How Can We Improve These Images?
Raman Venkataramani
YoramBresler
Sparsity constraints
Time-frequency transforms to accommodate variability of reflectance with respect to incident angle
Level-set methods
Deformable parametric contours
Yong Wu
Dave Munson
YoramBresler
Jeffrey Brokish
Schmid
Natalia

23. Autofocus Algorithms Essential for Processing Real Data
Dave Munson
Autofocus Algorithms Essential for Processing Real Data
Rob Morrison
Phase data corrupted by inaccuracies in estimates of distance to target
But most of the information is in the phase, not the magnitude!
Fortunately, errors along various transmitter-receiver pairs are related (similar to “phase closure” in radio astronomy)
Primary remaining challenge to forming images from real passive radar data

24. Completed Work
Fast O(N^2 log N), instead of O(N^3), projection and backprojection algorithm, adapted to SAR imaging
Good for curved projections for near-field imaging
Compatible with a wide variety of autofocus algorithms
Distorted Born Iterative Method (DBIM) for reconstruction of metallic scatterers in nonlinear scattering
Confidence region bounds for 2-D and 3-D shape estimation problems in both linear and nonlinear scattering problems
Shu
Xiao
Dave Munson
Michael Brandfass
Chew
Weng
Jong Ye
YoramBresler
Pierre Moulin

25. Completed Work (Con’t)
Michael Brandfass
Lanterman
Aaron
Comparison of the Colton-Kirsch “linear sampling” method with linearized tomographic algorithms
Antenna spacing selection for interferometric imaging radar using more than two antennas
Linear algorithms for “holographic” imaging from polerimetric radar data
Shu
Xiao
Dave Munson
Michael Brandfass

26. To Learn More... Technical POC: Dr. Aaron Lanterman work: 217-333-9638
home:
Project website:
Many papers available for download!