Post on 07-Jun-2020
1 National Center for Sensors and Defense Systems Technology
Single-Pass Subsurface SAR
Focusing for Multiple Point Targets
USC, The Microwave Systems, Sensors and Imaging Lab (MiXIL)
Radar Conference
December 9, 2014
Riyadh, Saudi Arabia
KACST, National Satellite Technology Program (NSTP)
USC MiXIL
2
Outline
Introduction
- Application
- Advantages
- Interferometry and single-pass
Objective
Overview of approach
Building subsurface raw data
Estimating wave velocity
Estimating targets depths
Modified Range-Doppler Algorithm
Results
Modified Omega-K Algorithm (MωKA)
Summary and Conclusion
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Introduction (1)
The ability of detecting buried objects or characterizingsubsurface structures from air or space is of great scientificand operational interest
Some application areas are:
- Mapping ice depth and layering properties
- Characterizing soil properties at root zone, under forest canopies
- Locating sources of water or oil deep below the surface
- Exploring ancient artifacts and structures
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Introduction (2)
Most traditional ground penetrating radars need to be close to, or in contact
with, the ground
Airborne and spaceborne ground penetrating radar systems using SAR
techniques have a significant advantage over the surface-based ones due to
their rapid and synoptic surveying capabilities
Depth mapping and vegetation height mapping have been demonstrated
with SAR interferometry “ two flight paths” and tomography
Present work is an investigation of whether it is possible to achieve 3D
mapping with single-pass SAR
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Introduction (3)
InSAR and single-pass SAR
Sources:
-DLR/Moreira
-H. Zebker
http://www.stanford.edu/group/radar/
- Generally use two-pass operation with significant separation between the two
tracks so as to obtain look angle differences
- The phase difference between two measurements to infer path length
difference and hence height information
Antenna 1
Antenna 2
r1
r2
- Can the depth be estimated with single pass strip-map SAR?
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Objective
The overall objective is to render the true position of
subsurface point targets using a single-pass strip-map
synthetic aperture radar (SAR)
Specific objectives are:
Estimate the depth of point targets below surface
Estimate the range and azimuth positions of buried point targets
construct the desired image
Perform trade studies and error analysis to understand
limitations
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Overview of approach
Synthetic data are built to develop and validate the technique
Backscatter intensity is used to estimate subsurface permittivity
Three different methods used to estimate the depth
Two different modified processing approaches are used:
- Modified Range-Doppler processing algorithm
- Modified Omega-K processing algorithm
Assumptions:
- The point targets are buried in a homogeneous but arbitrary half space
- No volume scattering or subsurface interfaces
- Radar frequency and sensitivity allow sufficient penetration depth
- Ground surface is randomly rough
- Ground could be lossy
- Two co-pol channels are available
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Building subsurface raw data
The demodulated baseband signal from a single point target can be
represented by :
h Ro (s)
s
t
Point Target
x
z
Ro (s)h
Ro (s)h
hRo (s)
s
tPoint Target
x
z
R1(s)
Ro (s)
Ro (s)
R1(s)
R1(s)
h
h
2000 )/)(2(/)(4
),(csRtKjcsRfj
surfacerreeAstS
2
_0 )/)(2(/)(4),(
csRtKjcsRfj
Subsurfaceropticalsubsurfaceroptical eeAstS
Where :
10)( RRsR roptical
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Estimating wave velocity (1)
Iso-range surfaces are determined by subsurface wave
velocity Use co-pol backscatter from rough
surface to estimate 1:
- Assume there is a target-free region
- Estimate co-pol backscatter at Doppler centroid
for several range points
- Or do standard SAR processing to get shh , svv
- Use a model such as SPM to retrieve
parameters
- Other/more sophisticated models can be used
- If available, use different models for raw data
synthesis and r retrievalθ1
θ0
θ0
Ht
Et
xε1 = εr..ε0 ,μ1
ε0,μ0
Iso-range surface
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Estimating wave velocity (2)
Define cost function and iteratively solve for r
Model parameters Co-pol data (synthetic or real)
Inverse operator (cost function)
rms surface
roughness
Where ginv is forward model estimate of measured data
When raw data and retrieval use the same model, m can be estimated
very accurately: gives wave speed and ground attenuation
SPM used to
simulate raw data
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Estimating target Depth(1)
- Estimation using an approximated analytical inverse model of subsurface Doppler
Different approaches are considered to estimate the depth
- Estimation from amplitude data ~2% error
- Estimation using near accurate numerical inverse model of subsurface Doppler
‘iterative technique’’ ~1% error
- IEEE-APS & URSI/USNC (2015), Vancouver
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Estimating target depth(2)
Subsurface target Doppler frequency
Ko
K1
hBoundary
d
l3
l4l2
l1
P1P2 ut
θ1θ2
Point TargetK0 and K1 = wave numbers
Unambiguous only if d is known!
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Estimating targets depths (1)
What if we assume refraction is negligible?
Ko
K1
h
d
l3
l1
P1P2 ut
θo
Point Target
δ
θo≈θ1
Boundary
θ1
l4l2
Can solve for d uniquely, but is it good?
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Estimating targets depths (3)
Platform altitude 4.7 km, lossless ground
Depth is not estimated well under this approximation; need
another method
Example 1 Example 2 Example 3 Example 4
Dielectric
constant (εr)
8 8 16 16
d (m) 5 10 5 10
Estimated d (m) 3.7 5.1 2.9 4. 8
Estimated (εr) 8.01 8.02 16.1 16.1
d Estimation
error
26% 49% 42% 52%
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Modified Range-Doppler Algorithm(1)
Range Doppler Algorithm (RDA)Raw data
Range
Compression
RCMC
Azimuth
Compression
Image reconstructed
Azimuth FFT
Range
Compression
Performing FFT followed by a range matched
filter multiply and finally a range IFFT
Matched filter used
Azimuth FFT
Transforming the data into range Doppler
domain using an azimuth FFT
Matched filter used
RCMC
Azimuth time
Range time
synthetic Aperture
Signal trajectories from point targets at two different range
Performing RCMC to straighten out a family of target
trajectories in order for them to run parallel to the
azimuth frequency axis.
The shift in range that is needed to align the signal
trajectory in a single range bin is determined for each
azimuth frequency bin. The straightened trajectories
are shown by the dotted lines
Performing at each range gate an azimuth
matched filtering. Azimuth frequency
Target spectra trajectories in Range-Doppler domain
Azimuth bandwidth
Range time
Azimuth
Compression
Image reconstructed
Transforming the data back to the time domain
using IFFT
)()/4(* 0)(SRi
eSC
C*(S) eiKt2
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Modified Range-Doppler Algorithm(2)
Modified Range and Azimuth Filters
h
d
R0
θinc
Point Target
Roptical
θtr
Rd
R1
Calculating Rd:
Modified Range filter
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Modified Range-Doppler Algorithm(3)
Modified Range filter
The idea here is to shift back the Range
compressed data by the correct amount
of slant range bins
Old slant range bin
Adjusted slant
range bin
Azimuth time
Range time
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Modified Range-Doppler Algorithm(4)
Modified Range and Azimuth Filters
Modified Azimuth filter
h Ro (s)
s
t
Point Target
x
z
Ro (s)hRo (s)
h
h Ro (s)
s
tPoint Target
x
z
R1(s)
Ro (s)
Ro (s)
h
h
10)( RRsR roptical
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Results(1)
Image reconstruction analysis
• P band
parameters values
Center frequency 500 MHz
Altitude 4.7 km
Band width 10 MHz
Pulse duration 4e-6 sec
Platform speed 200 m/sec
Pulse repetition frequency 600 Hz
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Results(2)
Case 1:
Standard case, point target is on the surface; to confirm the
basic performance of the RDA.
Point target coordinates: <0,2500,0> meters
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Results(3)
Case 2: This is where a point target is located in the subsurface,
assumed lossless, at a depth of 5m with permittivity of 8.
Point target coordinates: <0,2500,-5> meters
Before using the filtersAfter using the filters
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Results(4)
Case 3:
Point target is located in the subsurface, assumed lossless,
at a depth of 10m with permittivity of 16.
Point target coordinates: <0,2500,-10> meters
After using the filtersBefore using the filters
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Results(5)
Case 3: Permittivity: (6,0.15)
Multiple point targets: <1000,3000,-10>, <0,2500,-5> ,<-1000,2000,0>
- Applying one time filtering is possible using the estimated depths directly
- Optimal resolution can be obtained iterating around the estimated permittivity and depths
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Modified Omega-K Algorithm (MωKA)
• P band
parameters values
Center frequency 450 MHz
Altitude 5 km
Band width 12 MHz
Pulse duration 4e-6 sec
Platform speed 150 m/sec
Pulse repetition frequency 600 Hz
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Modified Omega-K Algorithm (MωKA)
- This modified algorithm maintain promising results nearly close to optimal desired
resolution and position accuracy
- Depth and its estimation also Roptical(s) affect the position accuracy and resolution expected
- Place of improvement is still expected, this work will be presented in this coming IGARSS2015,Milan
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Summary and Conclusion
The proposed processing algorithm, which integrates SAR focusing and
subsurface velocity profile estimation, is applied to point targets buried at
various depths for different SAR parameters and produced very good results
under given approximations
The errors in locating the target, image resolution in lateral and depth
dimensions, and errors in estimates of the ground dielectric properties were
quantified
This works shows the possibility of forming 3D images without interferometry or
tomography, by taking advantage of signal backscattered intensities and
polarization diversity and subsurface Doppler information.
Current work include high squint case analyses for both algorithms, validate the
technique with scaled real measured raw data “lab experiment”, and the extend
of distributed targets problem.
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USC MiXIL
Thank you for listening
Majid Albahkali1042 Downey Way
Denney Research Center (DRB) 140
226 ,MiXIL
Los Angeles, CA 90089-1111
Emails:
mbahkali@kacst.edu.sa
albahkal@usc.edu
http://mixil.usc.edu/people/students/majid-albahkali.htm