On the use of Regularizers for Microwave Inverse Problems · 2018-06-07 · 3 Given the effects (EM...
Transcript of On the use of Regularizers for Microwave Inverse Problems · 2018-06-07 · 3 Given the effects (EM...
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Benjamin Fuchs
Université de Rennes 1 - IETR, France
email: [email protected]
webpage: http://perso.univ-rennes1.fr/benjamin.fuchs/
On the use of Regularizers
for Microwave Inverse Problems
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Given the effects (EM field), determine the causes (EM currents, scattering map,tissues characteristics, etc.)
Microwave Inverse Problems - Examples
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Given the effects (EM field), determine the causes (EM currents, scattering map,tissues characteristics, etc.)
Many applications
biomedical imaging, antenna diagnostic, humanitarian demining, archeologicprospection, security screening, etc…
Microwave Inverse Problems - Examples
DIATOOL, TICRA - DKM. Popovic, McGill University - CA
[2-4] GHz
Duke University – USA
[17.5-26.5] GHz
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From (possibly noisy) observations y, retrieve the signal x
Inverse Problems – Formulation
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From (possibly noisy) observations y, retrieve the signal x
Inverse problems are (very) often ill-posed
non uniqueness
ill conditioning (different causes lead to similar effects)
Inverse Problems – Formulation
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Ill posedness => Use of regularizer & prior knowledge
Inverse Problems – Regularizer
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Ill posedness => Use of regularizer & prior knowledge
Inverse Problems – Regularizer
data fitting regularity
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Ill posedness => Use of regularizer & prior knowledge
l > 0 regularization parameter
p=2 - Tikhonov – smooth solutions
p=1 - LASSO – sparse solutions
Inverse Problems – Regularizer
data fitting regularity
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Antenna diagnostic
with L. Le Coq and M.D. Migliore (U. of Cassino - IT)
Inverse problem: identify antenna defaults from its radiated field
Motivations : radiating structures are more and more complex (active elements)
non invasive method, well suited to on site diagnostic
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Antenna diagnostic
with L. Le Coq and M.D. Migliore (U. of Cassino - IT)
Inverse problem: identify antenna defaults from its radiated field
Motivations : radiating structures are more and more complex (active elements)
non invasive method, well suited to on site diagnostic
Our approach: exploit of priori knowledge to reduce the number of measurements
Differential scenario => sparsity prior
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Antenna diagnostic
Inverse problem – choice of the regularizer (which p-norm?)
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Antenna diagnostic
Inverse problem – choice of the regularizer (which p-norm?)
L1 minimization (point-wise sparsity)
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Antenna diagnostic
Inverse problem – choice of the regularizer (which p-norm?)
L1 minimization (point-wise sparsity)
L1/L2 minimization (group-wise sparsity)
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Antenna diagnostic
Inverse problem – choice of the regularizer (which p-norm?)
L1 minimization (point-wise sparsity)
L1/L2 minimization (group-wise sparsity)
TV-norm minimization (gradient sparsity)
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Antenna diagnostic
Application – reflectarray measurements @ 12GHz – 193 cells
Thalès Alenia Space – Project R3MEMS
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Antenna diagnostic
Back propagation 1024 points
Matrix inversion900 points
Application – reflectarray measurements @ 12GHz – 193 cells
Thalès Alenia Space – Project R3MEMS
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Antenna diagnostic
Back propagation 1024 points
Matrix inversion900 points
Application – reflectarray measurements @ 12GHz – 193 cells
Thalès Alenia Space – Project R3MEMS
Compressive sensing64 points
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Antenna diagnostic
Application – reflectarray measurements @ 12GHz – 193 cells
Compressive sensing approaches
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Computational imaging system
with M. Davy and C. Yoya Tondo
Goal: design of a fast & low cost microwave imaging system with high resolution
Low cost: Use of chaotic cavity: spatial diversity => frequential diversityTransfer the effort from physical layer to softwareFaster than SAR & cheaper than phased arrays !
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Computational imaging system
with M. Davy and C. Yoya Tondo
Goal: design of a fast & low cost microwave imaging system with high resolution
Low cost: Use of chaotic cavity: spatial diversity => frequential diversityTransfer the effort from physical layer to softwareFaster than SAR & cheaper than phased arrays !
High resolution => Regularized inverse problem!
y(w) = H(w,r) s(w)
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Computational imaging system
with M. Davy and C. Yoya Tondo
First resultsX bandcavity of 0.5 x 0.5 x 0.3 m3
Time reversal Least squares Norm l1 min
Active imaging
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Computational imaging system
with M. Davy and C. Yoya Tondo
First resultsX bandcavity of 0.5 x 0.5 x 0.3 m3
Time reversal Least squares Norm l1 min
Active imaging
Synthetic “target”
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Fast antenna characterization – NEAR FIELD
with L. Le Coq, B. Uguen, M.D. Migliore (U. Cassino – IT) & S. Rondineau (UnB - BR)
Goal: interpolation to speed up planar near field measurements
Inverse problem: given a small number of measurements, find the missing field
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Fast antenna characterization – NEAR FIELD
with L. Le Coq, B. Uguen, M.D. Migliore (U. Cassino – IT) & S. Rondineau (UnB - BR)
Goal: interpolation to speed up planar near field measurements
Inverse problem: given a small number of measurements, find the missing field
Main idea: only a priori knowledge, EM field => low complexity
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Fast antenna characterization – NEAR FIELD
Low complexity => minimum rank => minimum nuclear norm
Regularized inverse problem!
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Fast antenna characterization – NEAR FIELD
Sampling step of 2l=> 4 times less meas. points !
Low complexity => minimum rank => minimum nuclear norm
Regularized inverse problem!
SGH @60GHz
GBH @60GHz
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Goal: speed up spherical measurements (NF & FF)
Main idea: from coarse field sampling => projection on spherical harmonics with sparsity prior => Regularized inverse problem!
Fast antenna characterization – FAR FIELD
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Goal: speed up spherical measurements (NF & FF)
Main idea: from coarse field sampling => projection on spherical harmonics with sparsity prior => Regularized inverse problem!
To identify N modes, N/3 measurement points only!=> 70% saving !
=> More efficient use of FF measurement facilities
Coarse sampling(RA @12GHz)
Fast antenna characterization – FAR FIELD
Projection on SH
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QUESTIONS ?
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Collaborators: M. Bjelogrlic (EPFL - CH) and M. Mattes (DTU - DK)
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Differential microwave imaging
Chalmers
-
Microwave imaging active technique to reconstruct a scene from emission/receptionof microwave
Advantages non invasive, non ionizing, low power, low cost
Targeted applications detection of tumor or brain stroke
differential scenario to follow the pathological evolution
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Direct problem (EPFL-LEMA)
Very heterogeneous medium (high contrast and losses)
Large scale problem (100x100x100 voxels)
=> Formulation VIE
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Differential microwave imaging
Direct problem
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Direct problem (EPFL-LEMA)
Very heterogeneous medium (high contrast and losses)
Large scale problem (100x100x100 voxels)
=> Formulation VIE
Inverse problem
Non linear => quantification of the non linearity degree (Born)
Ill posed => regularizer to stabilize the inversion
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Differential microwave imaging
Inverse problem
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Differential microwave imaging
Numerical results – simple model
Incident fields = 32 plane waves @ 1GHz
SNR=70dB
Reconstruction with regularizers
Differential scenario
Truncated SVD
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Differential microwave imaging
Numerical results – Duke model
Incident fields = 32 plane waves @ 1GHz
SNR=20dB
Next step: 3D problem
The Virtual Family-development of surface-based anatomical models of two adults and two children for dosimetric
simulations, Physics in Medicine and Biology, 2010.
Virtual Family Duke (IT’IS)
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Antenna diagnostic
Numerical Application – array of 10x10 open ended waveguides @10GHz
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Antenna diagnostic
Numerical Application – array of 10x10 open ended waveguides @10GHz
Back propagation 1024 points
Matrix inversion225 points
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Antenna diagnostic
Numerical Application – array of 10x10 open ended waveguides @10GHz
Compressive sensing approaches