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

3

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

5

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

22

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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