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Compressive Sensing for Real-Time Microwave Imaging Systems. Student : Hamed Kajbaf Faculty Advisor : Dr. Yahong Rosa Zheng Electrical and Computer Engineering Department Missouri University of Science and Technology, Rolla, MO 65409, USA. a x. a y. Measurement Grid. y. x. -z. - PowerPoint PPT Presentation
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Compressive Sensing for Real-Time Microwave Imaging SystemsStudent: Hamed Kajbaf Faculty Advisor: Dr. Yahong Rosa Zheng
Electrical and Computer Engineering DepartmentMissouri University of Science and Technology, Rolla, MO 65409, USA
Results Performance and ComplexityOriginal DCT CS DFT CS
3D
z = - 34
z = - 65
z = - 81
y (mm)
x (m
m)
0 30 60 90 120
0
30
60
90
120
150
180
y (mm)
x (m
m)
0 30 60 90 120
0
30
60
90
120
150
180
y (mm)
x (m
m)
0 30 60 90 120
0
30
60
90
120
150
180
y (mm)
x (m
m)
0 30 60 90 120
0
30
60
90
120
150
180
y (mm)
x (m
m)
0 30 60 90 120
0
30
60
90
120
150
180
y (mm)
x (m
m)
0 30 60 90 120
0
30
60
90
120
150
180
y (mm)
x (m
m)
0 30 60 90 120
0
30
60
90
120
150
180
y (mm)
x (m
m)
0 30 60 90 120
0
30
60
90
120
150
180
y (mm)
x (m
m)
0 30 60 90 120
0
30
60
90
120
150
180
0 10 20 30 40 50 60 70 80 90 1000
0.05
0.1
0.15
0.2
Data percentage
Norm
aliz
ed R
MS
err
or
DFT CSDCT CS
0 10 20 30 40 50 60 70 80 90 1000
1
2
3
x 104
Data percentage
Iter
atio
ns
DFT CSDCT CS
Conclusion Using CS for wideband SAR imaging saves 51%
of the acquisition time.
Application of CS to Wideband SAR Sparse representation in DFT and DCT domains
Recovering using basis pursuit (BP)
cΦΨycc
~subject to~min 1C~
Ν
Undersampled measurements Sampling matrix
Sparse representation Sparsifying matrix
Wideband Near-field SAR Imaging Application: Nondestructive Testing (NDT)
3D millimeter wave strip-map SAR imaging
Advantage: High resolution, depth penetration, nondestructive
Disadvantage: Slow raster scanning with traditional uniform grid full measurement
Hardware Setup
z0
Measurement Grid
Sample Under Test
x
y
-z
ay
ax
,, yxf
zyxs ,,
c~
yΦ
Ψ
Experimental Tests 9 round rubber pads: 5 mm diameter
Swept frequency in 35.04 to 44.96 GHz (Q-band)
Steps of 2mm along X and Y direction
Complete data: 2947 sec
Undersampled: 35% of spatial points: 1450 sec
Future Work Use the 3D SAR as the sparse representation to
improve the quality of the recovered images.
Make the selection of sparse representation adaptive.
Acknowledgements This work was supported in part by the Intelligent
System Center, ASNT fellowship, and by University of Missouri Research Board fund.
Objective Acquire reflection coefficients at random
position and frequencies to reduce the acquisition time.
Recover the SAR images applying compressed sensing (CS) using 35% of spatial and 20% of frequency samples
0
2224
211
2 ,,,,zkkkj
DNUDyxeyxfzyxs FFF
2D Fourier transform operator
2D inverse Fourier transform operator
Inverse nonuniform Fourier transform operator
Wavenumber
Fourier transform variable corresponding to x
Fourier transform variable corresponding to y
1NUF
D2F1
2DF
xkk
yk
, ,f u v
, ,s x y z
SAR Image
Reflection Coeff.
Image Reconstruction