3. • Abstract:
Current SAR/ISAR imaging algorithms rely upon the assumption
that the area under observation consists of a superposition of
infinitesimally small isotropic scatterers (i.e., the point
scatterer model).
This approximation fails to capture the real-world scattering
mechanisms occurring within the targets under illumination.
This paper proposes an imaging technique based upon the
assumption that targets may be modeled as a superposition of
infinitesimally small dipoles. The orientation of each dipole is
encapsulated in a dyadic contrast function.
1
, ,
, ,
Scattered field
Tr r t t
sca
D
P
Tr r t t
n n n
n
e K d
Q
a G r r V r G r r a r
a G r r V r G r r a
,
Incident fie
t t
l
Inc
d
e r G r r a
1 11 1 11
1
...
...
xx xx xy xy xz xz
mp mp mp mp mp mp mp
yx yx yy yy yz yz
mp mp mp mp mp mp
zx zx zy zy zz zz
mp mp mp mp mp mp
T
mp mp
T T
P
T T
M M MP MP
e l v l v l v
l v l v l v
l v l v l v
e
e
y L
l v
l l v
l l v
FORMATION OF A MATRIX EQUATION
H
x
y L x
x L y
Conclusions:
Objects are imaged only when the sensor polarization
and the infinitesimally small (z-directed) dipole are
aligned.
Therefore, using the dipole-based model one can
determine the orientation of such dipoles, thus providing
additional information concerning the target.
Note that if a point-scattering model were used, all
figures would appear equal.
The cylinder is oriented along z - axis while the antenna
is along the x - axis. No detection if the target is not
aligned with incident field.
The cylinder and the antenna are both oriented along
z - axis. The target is detected due to the same alignment
with incident field
X Antenna
Probe
Y
Cylindrical Target
Z
The cylinder is oriented along z - axis while the
antenna is along the y - axis. No detection if the
target is not aligned with incident field
Experimental image of vertically oriented
cylinder with vertically polarized Tx and Rx.
Superposition of point sources Superposition of small dipoles
Source and small dipole model 3D cylinder
MUMMA RADAR LAB
A Dyadic Target Model for Multistatic SAR/ISAR Imaging
Ali Nassib Tadahiro Negishi Danilo Erricolo Michael C. Wicks Lorenzo Lo Monte
6. Experiment Geometry
EMI Rejection Experiment
Coherent Fusion Imaging Experiment
Summary
EMI Rejection- Post cancellation SNR of target detection with respect to electromagnetic
interference (EMI) was improved. The proposed MIMO radar configuration can be employed
jointly to radar and communications systems sharing the same spectrum to improve isolation
between them.
Coherent Fusion Imaging- Demonstrated the potential of orthogonal waveforms in distributed
aperture radar architectures (DAR) for achieving improved resolution, interference suppression,
and target detection and tracking performance while simultaneously controlling space-
frequency grating lobes. Adaptive processing using frequency diversity was simulated and
demonstrated. A knowledge-aided algorithm was employed to estimate the covariance matrix
needed for the adaptive processing algorithm.
Distributed Apertures for Robustness in Radar and Communications (DARRC)
Improved Electromagnetic Interference Rejection & Detection of Over Resolved Targets
V. J. Amuso, R. A. Schneible and Y. Zhang
Upstate Scientific
New Hartford NY 13413 USA
amuso@upstatescientific.com
Michael C. Wicks
University of Dayton, ECE Department
Dayton OH 45469 USA
mwicks1@udayton.edu
Overview
Two robust MIMO approaches have been investigated for improved isolation. A distributed
aperture radar (DAR) architecture is used in both. The aperture spacing differs in the two
approaches. In the first approach the apertures are closely spaced when viewed from the target
(radar) or remote node (communications). This approach involves small bistatic angles. The
second approach uses widely spaced apertures in a classical multi-static configuration; this
approach involves arbitrary bistatic angles.
• The first technique is used for improved EMI rejection for both radar and communications
systems.
• The second technique is used for improved detection of over resolved targets for multiple-
input multiple-output (MIMO) radar systems using coherent fusion imaging.
REFERENCES
[1] E. Fishler, et.al., “MIMO Radar: an idea whose time has come,” Proceedings of the IEEE 2004 Radar Conference, Philadelphia, Pennsylvania, April 2004.
[2] F. Robey, et. al, “MIMO radar theory and experimental results,” Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, November 2004.
[3] P. Stoica, et.al., “On Probing Signal Design For MIMO Radar,”IEEE Transactions on Signal Processsing (Volume: 55, Issue: 8), August 2007.
[4] A. De Maio, et. al., “Design Principles of MIMO Radar Detectors,” IEEE Transactions on Aerospace and Electronic Systems (Volume: 43, Issue:3 ), November 2007.
[5] M. C. Wicks, B. Himed, H. Bascom, and J. Clancy, “Tomography of Moving Targets for Security and Surveillance”, Proc. 2005 NATO Advanced Study Institute, IL Ciocco, Italy, July
2005.
Image of two Large Scatterers Driven Through the
Radars’ Field-of-View
Angle Relative to Interfering Source (milliradians)
Target Detection vs. Angle
EMI Rejection Experimental Results
Experimental data was collected and adaptively processed using sample matrix inversion (SMI)
for a scenario where a target vehicle traveled along a course that brought it past an interference
source.
Coherent Fusion Imaging Results
Experimental data was collected to successfully demonstrate an improvement in radar imaging
capability by using data from both radars compared to mono-static data from single radar.
Fourier sampling for three sensors (two radars operating both mono-statically and bi-statically).
The aqua and blue sectors are the Fourier sampling for the mono-static operation of these two
radars. The green sector represents the bi-static sampling. For this geometry (ISAR images as
the target flies through the field of view of two radars separated by 90°, with 50% bandwidth)
the combined multi-static image would have a resolution 2.5 times better than a single mono-
static image.
Flick run of the EMI scene along side the cancelled EMI scene. The frame
rate is 2 seconds with each of the range Doppler frame plots recorded over a
256 millisecond interval at a 1 kHz rate.
t= 10 seconds t= 14 seconds
t= 16 secondst= 12 seconds
