Transcript Document

TH E U N IVE RSITY O F BR ITI SH C OLU MB IA
Digital Processing Algorithms for Bistatic
Synthetic Aperture Radar Data
4 May 2007
by Y. L. Neo
Supervisor : Prof. Ian Cumming
Industrial Collaborator : Dr. Frank Wong
Sponsor : DSO National Labs (Singapore)
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Agenda
•
•
•
Bistatic SAR principles
A Review of Processing Algorithms
Contributions
1.
2.
3.
4.
•
Point Target Spectrum
Relationship Between Spectra
Bistatic Range Doppler Algorithm
Non Linear Chirp Scaling Algorithm
Conclusions
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Bistatic SAR
• In a Bistatic configuration, the Transmitter and Receiver
are spatially separated and can move along different
paths.
• Bistatic SAR is important as it provides many advantages
– Cost savings by sharing active components
– Improved observation geometries
– Passive surveillance and improved survivability
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Imaging geometry of monostatic/bistatic SAR
eam
Transmitter B
RT
Transmitter
Monostatic
SAR Platform
RT
Target
Target
Bistatic Angle 
2x
ht Path
line
Receiver Flig
e
Bas
Transmitter
Flight Path
Platform flight
path
RR
R
ive
e
ec
ea
B
r
m
Receiver
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Focusing problems for bistatic algorithms
•
Traditional monostatic SAR algorithms based on
frequency domain methods make use of 2
properties
1. Azimuth-invariance
2. Hyperbolic Range Equation
•
Bistatic SAR data, unlike monostatic SAR data,
is inherently azimuth-variant
– targets having the same range of closest approach do
not necessarily collapse into the same trajectory in the
azimuth frequency domain.
•
Difficult to derive the spectrum of bistatic signal
due to the double square roots term (DSR).
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Agenda
•
•
•
Bistatic SAR principles
A Review of Processing Algorithms
Contributions
1.
2.
3.
4.
•
Point Target Spectrum
Relationship Between Spectra
Bistatic Range Doppler Algorithm
Non Linear Chirp Scaling Algorithm
Conclusions
6 out of 30
Overview of Existing Algorithms
• Time domain algorithms are accurate as it uses the
exact replica of the point target response to do
matched filtering
• Time based algorithms are very slow – BPA, TDC
• Traditional monostatic algorithms operate in the
frequency domain
– RDA, CSA and ωKA
– Efficiency achieved in azimuth frequency domain by
using azimuth-invariance properties
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Existing Bistatic Algorithms
•
Frequency based bistatic algorithms differ in
the way the DSR is handled.
•
Majority of the bistatic algorithms restrict
configurations to fixed baseline.
•
Three Major Categories
1.
2.
3.
Numerical Methods – ωKA, NuSAR – replace transfer
functions with numerical ones
Point Target Spectrum – LBF
Preprocessing Methods – DMO
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LBF (Loffeld’s Bistatic Formulation)
• Solution for the stationary point is a function of azimuth
time given in terms of azimuth frequency (f )
• LBF derived an approximation solution - b(f) to
stationary phase
Approximate
Solution to
Stationary phase
Using this relation, the analytical point target spectrum can be
Formulated - LBF
Quasi-monostatic
Term
Bistatic
Deformation Term
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DMO (Dip and Move Out)
• Transform Bistatic data to Monostatic (using Rocca’s
Smile Operator)
• Assumes a Leader-Follower flight geometry
(azimuth-invariant)
Phase modulator
Migration operator
Rocca’s smile operator
transforms
Bistatic Trajectory
to
Monostatic Trajectory
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Agenda
•
•
•
Bistatic SAR principles
A Review of Processing Algorithms
Contributions
1.
2.
3.
4.
•
Point Target Spectrum
Relationship Between Spectra
Bistatic Range Doppler Algorithm
Non Linear Chirp Scaling Algorithm
Conclusions
11 out of 30
Major Contributions of the Thesis
#1
Derived an accurate
point target spectrum using MSR
(Method of Series Reversion)
#3
Derived Bistatic RDA Applicable to
Parallel flight
cases with fixed
baseline
#2
Compare Spectrum
Accuracy MSR is more accurate than
Existing point target
Spectrum – LBF and
DMO
#4
Derived NLCS
Algorithm – Applicable
to Stationary
Receiver & Non-parallel
Flight cases
Focused Real bistatic
data. Collaborative
work with U. of Siegen
(airborne-airborne data)
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Derivation of the analytical bistatic point
target spectrum
• Problem : To derive an accurate analytical Point
Target Spectrum
• POSP: Can be used to find relationship between
azimuth frequency f and azimuth time 
f = [1/(2)] d()/d
• But we have to find  = g(f ).
Difficulty: phase () is a double square root.
# 1
2
3
4
Contribution
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Our Approach to solving the point target
spectrum
•
Approach to problem:
– Azimuth frequency f can be expressed as a
polynomial function of azimuth time .
– Using the reversion formula,  can be expressed
as a polynomial function of azimuth frequency f
•
Journal Paper Published :
Y.L.Neo., F.H. Wong. and I.G. Cumming A two-dimensional spectrum for
bistatic SAR processing using Series Reversion, Geoscience and Remote
Sensing Letters, Jan 17 2007.
# 1
2
3
4
Contribution
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Series Reversion
• Series reversion is the computation of the coefficients
of the inverse function given those of the forward
function.
# 1
2
3
4
Contribution
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New Point Target Spectrum
Method of Series Reversion (MSR)
• An accurate point target spectrum based on power series is
derived
• Solution for the point of stationary phase is given by
• The accuracy is controlled by the degree of the power series
• MSR can be used to adapt Monostatic algorithms to process
bistatic data
- RDA and NLCS
# 1
2
3
4
Contribution
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Linking - MSR, LBF and DMO
• We established the Link between MSR, LBF and DMO
• Using the MSR, we derived a new form of the point
target spectrum using two stationary points.
• Similar to LBF, the phase of MSR can be split into
quasi-monostatic and bistatic deformation terms.
• Journal Paper Submitted:
Y.L.Neo., F.H. Wong. And I.G. Cumming A Comparison of Point Target Spectra Derived for Bistatic SAR
Processing, Transactions for Geoscience and Remote Sensing, submitted for publication,14 Dec 2006.
# 1
2
3
4
Contribution
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Link between MSR and LBF
Stationary point solutions
MSR
LBF
Split phase into quasi monostatic and bistatic components
# 1
2
3
4
Contribution
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LBF and DMO
• Rocca’s smile operator can be shown to be
LBF’s deformation term if the approximation
below is used
Approximation is valid when
baseline is short when
compared to bistatic Range
# 1
2
3
4
Contribution
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Alternative method to derive the
Rocca’s Smile Operator
# 1
2
3
4
Contribution
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Link between MSR, LBF and DMO
2D Point Target Spectrum
MSR
Expand about Tx and Rx stationary points
Consider up to Quadratic Phase only
Quasi-Monostatic
Term
LBF
Leader - Follower
Flight configuration
Monostatic
Term
DMO
# 1
2
3
4
Contribution
Bistatic
Deformation Term
Baseline is short
Compared to Range
Rocca’s Smile
Operator
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Bistatic RDA
• Developed from the MSR 2D point target spectrum
• Monostatic algorithms like RDA, CSA achieve
efficiency by using the azimuth-invariant property
• Bistatic range histories are azimuth-invariant by if
baseline is constant
• The MSR is required as range equation is not hyperbolic
• Journal Paper Reviewed and Re-submitted:
Y.L.Neo., F.H. Wong. And I.G. Cumming Processing of Azimuth-Invariant Bistatic SAR Data
Using the Range Doppler Algorithm, IEEE Transactions for Geoscience and Remote Sensing,
re-submitted for publication, 12 Apr 2007.
# 1
2
3
4
Contribution
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Main Processing steps of bistatic RDA
Baseband Signal
# 1
2
3
4
Range FT
Azimuth FT
RCMC
Range Compression
Azimuth Compression
SRC
Azimuth IFT
Range IFT
Focused Image
Contribution
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Real Bistatic Data Focused using Bistatic
RDA
Copyright © FGAN FHR
# 1
2
3
4
Contribution
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Non-Linear Chirp Scaling
• Existing Non-Linear Chirp Scaling
– Based on paper
F. H. Wong, and T. S. Yeo, “New Applications of Nonlinear Chirp Scaling in SAR
Data Processing," in IEEE Trans. Geosci. Remote Sensing, May 2001.
– Assumes negligible QRCM (i.e., for SAR with short wavelength)
– Shown to work on Monostatic cases and the Bistatic cases where
receiver is stationary
• We have extended NLCS to handle Bistatic nonparallel tracks cases
# 1
2
3
4
Contribution
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Extended NLCS
• Able to handle higher resolutions, longer
wavelength cases
• Corrects range curvature, higher order phase
terms and SRC
• Develop fast frequency domain matched filter
using MSR
• Solve registration to Ground Plane
• Journal Paper written:
F.H. Wong., I.G. Cumming and Y.L. Neo, Focusing Bistatic SAR Data using Non-Linear
Chirp Scaling Algorithm, IEEE Transactions for Geoscience and Remote Sensing, to be
submitted for publication, May 2007.
# 1
2
3
4
Contribution
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Main Processing steps of Extended NLCS
• NLCS applied in the time domain
• SRC and Range Curvature Correction --- range
Doppler/2D freq domain
• Azimuth matched filtering --- range Doppler domain
Baseband
Signal
Range compression
The NLCS scaling function is a
polynomial function of azimuth time
Azimuth compression
Focused
Image
# 1
2
3
4
Contribution
LRCMC / Linear
phase removal
Range
Non-Linear
Curvature
Correction
Chirp Scaling
and SRC
Range
Non-Linear
Curvature
Chirp
Correction
Scaling
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Agenda
•
•
•
Bistatic SAR principles
A Review of Processing Algorithms
Contributions
1.
2.
3.
4.
•
Point Target Spectrum
Relationship Between Spectra
Bistatic Range Doppler Algorithm
Non Linear Chirp Scaling Algorithm
Conclusions
28 out of 30
Concluding Remarks
• With our four contributions, a more general and
accurate form of bistatic point target spectrum was
derived.
• Using this result, we were able to focus more general
bistatic cases using several algorithms that we have
developed.
• We plan to work on future projects that make use of the
results from this thesis
– Interest express from several agencies
– DRDC (Ottawa), DSO National Labs (Singapore) and U. of Siegen (Germany).
–
–
Satellite – Airborne (TerraSAR-X and PAMIR)
Satellite/Airborne – stationary receiver (X and C band) using RADARSAT-2 or TerraSAR-X
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Q&A You
Thank
30 out of 30