21 November 2007 Prometheus Inc. Company Proprietary 1 Prometheus … a team Dr. Myoung An Dr. Jennifer Brower Dr. Jim Byrnes Dr. Joseph J. Kranz Dr. Edmund Sullivan Dr. Richard Tolimieri
21 November 2007 Prometheus Inc. Company Proprietary 1
Prometheus … a team Dr. Myoung An Dr. Jennifer Brower Dr. Jim ByrnesDr. Joseph J. KranzDr. Edmund SullivanDr. Richard Tolimieri
21 November 2007 Prometheus Inc. Company Proprietary 2
Why are we here ?
• Share a mathematically derived processingmethodology which utilizes existing signal content to identify the nature of materials of the objects of interest
• Provide an approach to transition this proven radar scheme to sonar
• Suggest applications using the method to resolve difficult operational sensor employment problems for torpedos and other platforms
21 November 2007 Prometheus Inc. Company Proprietary 3
“Providing Mathematics forScience and Innovation”
Servicing the US Government and International Technology Community: DARPA, Army, Navy, Air Force, NATOFounded in 1983Small business under NAICS 541330 Woman-ownedBased in Newport, RI12 Doctoral Level Mathematicians and Scientists
21 November 2007 Prometheus Inc. Company Proprietary 4
Relevant Prometheus Technology
Waveform Design and DiversityAdaptive radar
Feature-Based Pattern RecognitionMine detection using Synthetic Aperture Sonar
Acoustic ModelingPro-Verb torpedo reverberation model
Inverse ProblemsMaterials Identification Synthetic Aperture Radar
21 November 2007 Prometheus Inc. Company Proprietary 5
Waveform Design and Diversity
Design of waveform sets with optimal correlation propertiesAdaptive selection of transmitted waveforms using echo information
dB-plot of correlations of two length 792 signals
Response with Prometheus waveforms
Typical radar response
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Feature Based Pattern Recognition
Undersea Mines
Originated as Prometheus IR&D program with Raytheon assistance.
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Feature Based Pattern Recognition
Mine Detection ROC Curve
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Feature Based Pattern Recognition
Design and implementation of spectral analysis toolbox for detection and classification of mine-like objects in high-resolution Synthetic Aperture Sonar data
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SAS Mine-Like Object Detection ROC Curve
Pd
False Calls per Image
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Meets or exceeds Weapons Analysis Facility (WAF) requirementsPulse length from 0.01 to 0.5 secondsCenter frequency of 10 kHz to 40 kHzBandwidth as high as 25% of center frequencySupports element-level time series data for 100+ elements
Pro-VerbTorpedo Reverberation Modeling
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MISAS
Materials Identification Using Synthetic Aperture Sonar
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• Based on MISAR, Materials Identification Synthetic Aperture RADAR, a proven method
• MISAR Phase I, II• USAF, Brooks City Base, Richard Albanese• Time-domain deconvolution algorithm development
– Phase III• National Reconnaissance Office (NRO)• Time-frequency space processing
MISAS foundations
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Suggested MISAS Applications
• Classification of targets in mid and low frequency ranges
• Reduction of false alarms
• Mine detection, localization & identification
• Reverberation discrimination in harsh environments
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OUTLINE
1. Background
2. Deconvolution
3. Algorithm
4. Discussion
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Reflectivity Kernel Estimation
Material properties of target summarized in reflectivity kernel R(t)
radar/sonartargetpulse
echo
echo = convolution of pulse and R + noise
Goal: Recover material information from noisy echo through deconvolution
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MISAS Process Diagram
EchoesDenoise andDeconvolve
withZak transform
A priori informationon pulse
BayesianStatistical test
A priori statisticalinformation
Comparison test
Library
Discriminationdecision
Identification decision
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Deconvolution
Given the input or “probe” signal, find the Kernel, K, from the received or scatteredsignal. K characterizes the material
This is an inverse problem. The associated forward problem is: Given K and an inputsignal s, find the scattered or output signal
∫ +′′′= noisexdtsttKtf )(),()(
Output Kernel Input
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Examples of Reflectivity Kernels
•Heavy metals•Composite materials
•Water•Biological tissue•Radar-absorbing urethane foam
Atmospheric interference
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The ILL-Posed Problem
Deconvolution is an inverse problem, therefore is “ill-posed,’’ which means it can be
1. Sensitive to noise2. Non-unique3. Subject to observability problems
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Observability Requirements
1. The information must be in the data
2. The signal must be sensitive to the desired data
3. The receiver must be sensitive to therelevant characteristics of the signal
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Example of Poor Observability
Consider a towed array attempting toestimate both range and bearing
If the range is much larger than the aperture, it is “weakly” observable and thus essentially impossible to estimate
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MISAS APPROACH
Gain observability with signal design
Denoise and deconvolve with the Zak Transform
Increase observability with SAS
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Based on the Zak Transform
∑+∞
−∞=
−=
k
Nnki
n eksfπ2
)(Fourier Transform
∑+∞
−∞=
−+=k
kfimnm
nektsftZ π2)(),(Zak Transform
The Zak transform is a time-frequency representation. Forour purposes, it provides a time-frequency map which “deconstructs” the signal and provides noise reduction.
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Nyquist Samples
Critical Samples ShiftedCritical Samples
1 M 2M 3M KM
Nyquist sampling vs “Critical sampling”KM=2 X Highest Freq. (Nyquist)K=2 X Bandwidth (Critical)
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Data Sample Map
003
02
01
113
12
11
321
K
K
MK
MMM
xxxx
xxxx
xxxx
LL
LL
MM
L Shift Right M Samples
Shift Right 1 Sample
No Shift
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Zak Time-Frequency Map
003
02
01
113
12
11
321
K
K
MK
MMM
ffff
ffff
ffff
LL
LL
MM
L
Tim
e
Frequency
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Zak Time-Frequency mapFor 2 Chirps
t Note that
Tt <<∆T
1t2t
ttt ∆=− 12
f
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Example For a Multi-Point Scatterer
We consider a multi-point scatterer and a linear frequency modulated (LFM) signal, or “chirp” as the probe.
The scattered signal then consists of multiple LFM signals mixed in space and time.
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Closely-Spaced Target Separation
Incident FM Chirp
Noisy Echo From Point Scatterers
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MISAS APPROACH
Gain observability with signal design
Denoise and deconvolve with the Zak Transform
Increase observability with SAS
21 November 2007 Prometheus Inc. Company Proprietary 36
MISAS Process Diagram
A priori statistical
EchoesDenoise andDeconvolve
withZak transform
A priori informationon pulse
BayesianStatistical test
Discriminationdecision
information
Comparison test
Library
Identification decision
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Improving Observability with Synthetic Aperture Sonar (SAS)
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SAS: Multiple Measurements
Use SAS to increase effective pulse duration by combining multiple measurements. Extends algorithm to cases where pulse duration < material relaxation time.
Multiple measurements at different aspects Increases cross-range resolution and observability
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SAS Geometry
Ideal along track path
Transmit/Receivelocations
x
y
z
Physical array Physical aperture
footprint
Synthetic aperture footprint(constant resolution)
Synthetic array
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Technical Issues
• Sensitivity
• Availability of data
• Defining meaningful experiments
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Suggested Applications
• Classification of targets in mid and low frequency ranges (coda)
• Reduction of false alarms
• Mine detection, localization & identification
• Reverberation discrimination in harsh environments
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Courtesy of Naval Undersea Warfare Center – Division, Newport,
Code 8133 (15 November 2007)
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Courtesy of Naval Undersea Warfare Center – Division, Newport, Code 8133 (15 November 2007)
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Next Steps• Develop SONAR version of MISAR
– Radar to Sonar conversion details• Concept modeling & validation• Develop library of reflectivity kernels
– NUWC Acoustic Test Facility– Advanced Processing Build (APB)
• Identify candidate host system• Perform technical and operational tests
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Available Funding Vehicles
• Phase III SBIR – USAF/NRO
• Materials Identification Synthetic Aperture Radar (MISAR) (new sensor application)
– NUWC Weapons Analysis Facility (WAF)
• ProVerb follow-on (reverberation mitigation)
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Initial Effort (First three “Next Steps”)
• Algorithm Conversion• Algorithm Validation/Testing
– Acoustic Test Facility• Kernel library development – steel, composites,
structural plastics, flesh, rubber, rock, sand, etc.• Experiment design/test fixture design-construction• Sensitivities to waveform, impulse intensity, etc.
• Physics issues research• Waveform/Signal Processing optimization• Source/receiver/transducer requirements
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Level of Effort/Period of Performance Estimation – Initial Effort
• Bullets 1-3 of “Next Steps” and parallel APB definition*~ 4 to 7 work-years
(Prometheus + NUWC, details of NUWC involvement TBD)~ 1 to 1.5 yearsDetailed research plan within 30 days
* Does not include development/coding of the APB, host system identification/integration and engineering required to conduct WAF ‘hardware-in-the-loop’ testing or at-sea testing of a prototype.
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ReferencesGroup Filters and Image ProcessingMyoung An and Richard TolimieriPsypher Press, 2003, ISBN 0-9741799-0-6
Time-Frequency RepresentationsMyoung An and Richard TolimieriBirkhauser, Applied and Numerical Harmonic Analysis Series,
1998, ISBN 0-8176-3918-7
Ideal Sequence Design in Time-Frequency SpaceMyoung An, A.K. Brodzik and Richard TolimieriBirkhauser, Applied and Numerical Harmonic Analysis Series, 2007, ISBN 0-8176-4737-6
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Thank You
Dr. Jim [email protected](781) 784-2355(401) 849-5389
21 November 2007 Prometheus Inc. Company Proprietary 50
Example of Convolution
SystemIN OUT
)( ttR ′−)( tt ′−δ
t′t′ tt
)(tfOUT)(tfIN
tt
)()()( ttRtftft
INOUT ′−′≅ ∑′
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Zak Transform of Chirp
∑+∞
−∞=
−+=k
kiektftZ πυυ 2)(),(
2
)( tietf π= )2( 22
)( kkttiektf ++=+ π
∑ −+=k
ktkiti eetZ )][2( 22
),( υππυ
2/10),( −−=≠ ktwhenonlytZ υυ
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tdttRtftft
INOUT ′′−′= ∫′
)()()(
)()()( 2211 ttattattR −+−=′− δδ
Recall
For the 2-point scatterer
Thus, the kernel has been extracted directlyfrom the time frequency map
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In The Limit
tdttRtftft
INOUT ′′−′= ∫′
)()()(
Or, More Generally
∫ +′′′= noisexdxsxxKxf )(),()(
Output Kernel Input
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Time-Frequency mapFor a Complex Scatterer
υ
t
R(t)
t