Inverse Synthetic Aperture Radar

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Hindawi Publishing Corporation
EURASIP Journal on Applied Signal Processing
Volume 2006, Article ID 63465, Pages 1–4
DOI 10.1155/ASP/2006/63465
Editorial
Inverse Synthetic Aperture Radar
Marco Martorella,1, 2 John Homer,3James Palmer,4Victor Chen,5Fabrizio Berizzi,1,2
Brad Littleton,6and Dennis Longstaff1
1The school of ITEF, The University of Queensland, Brisbane 4072, Australia
2Department of Information Engineering, University of Pisa, Via G. Caruso 16, 56122 Pisa, Italy
3School of Information Technology & Electrical Engineering, University of Queensland, Brisbane 4072, Australia
4Radar Modelling & Analysis Group, Electronic Warfare & Radar Division, Defence Science & Technology Organisation,
P.O. Box 1500, Edinburgh 5111, UK
5Naval Research Laboratory, 4555 Overlook Ave., SW Washington, DC 20375, USA
6Centre for Quantum Computer Technology, School of Physical Sciences, University of Queensland, Brisbane 4072, Australia
Received 2 March 2006; Accepted 2 March 2006
Copyright © 2006 Marco Martorella et al. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
cited.
Introduction to ISAR
Inverse synthetic aperture Radar (ISAR) is a powerful sig-
nal processing technique that can provide a two-dimensional
electromagnetic image of an area or target of interest. Be-
ing radar based, this imaging technique can be employed in
all weather and day/night conditions. ISAR images are ob-
tained by coherently processing the received radar echoes of
transmitted pulses. Commonly, the ISAR image is charac-
terised by high resolution along both the range and cross-
range directions. High resolution in the range direction is
achieved by means of large bandwidth transmitted pulses,
whereas high cross-range resolution is obtained by exploiting
a synthetic antenna aperture. In ISAR, the synthetic aperture
is generated by motion of the target as well as possibly by
motion of the radar platform. In contrast, the related imag-
ing technique of Synthetic aperture radar (SAR) has its syn-
thetic aperture generated by means of radar platform motion
only.
Initially, the name ISAR was derived from SAR by simply
considering a radar-target dynamic where the radar platform
was fixed on the ground and the target was moving around.
Today, however, it is understood that the basis of the differ-
ence between SAR and ISAR lies in the noncooperation of
the ISAR target. Such a subtle difference has led in the last
decades to a significant separation of the two areas. The non-
cooperation of the target introduces the main problem of
not knowing the geometry and dynamic of the radar-target
system during the coherent integration time. Such a limita-
tion leads to the use of blind radial motion compensation
(image autofocusing) and image formation processing that
must deal with highly nonstationary signals.
The SAR community is very large and the areas of inter-
est within SAR grow steadily each year. The ISAR community
is much smaller, in comparison, and it is often difficult to
bring together world leaders in this sector. This special issue
aims to gather the latest novelties in ISAR in order to pro-
vide an updated reference for current and future research in
this area. This has involved a comprehensive peer review pro-
cess to guarantee technical novelty and correctness. As dis-
cussed below, the presented papers, six in total, are equally
divided amongst the three primary areas of ISAR research,
namely: motion compensation (or image autofocusing), im-
age formation,andtarget classification/recognition.Whereas
the first two areas are devoted to the reconstruction of the
ISAR image, the latter concerns the use of the ISAR image
for target recognition—one of the principle motivations for
ISAR development.
Motion compensation
Motion compensation is the first step in the ISAR image re-
construction chain. Image focus and clarity strongly depend
on the accuracy of motion compensation. Often referred to
as image focusing or image autofocusing (blind data driven
motion compensation), the motion compensation problem
has been largely addressed since the beginning of ISAR. Sev-
eral algorithms have been provided that accomplish motion
compensation. Nonparametric algorithms such as promi-
nent point processing (PPP) and phase gradient algorithm

2 EURASIP Journal on Applied Signal Processing
(PGA) often, in the past, have been applied in ISAR imaging,
largely because they do not need a signal model assumption.
More recently, several other nonparametric methods, such
as the maximum likelihood- (ML-) based technique and the
joint time-frequency analysis (JTFA) technique, have been
proposed and are proving to be relatively effective. On the
other hand, parametric approaches, such as image-entropy
or image contrast-based algorithms, are attracting increased
attention due to the potential enhancements they can pro-
vide over nonparametric approaches.
In this special issue, two papers are presented which
address the problem of motion compensation. The first,
written by Martorella et al., concerns a general exten-
sion of two parametric algorithms, namely, the image con-
trast based-algorithm (ICBA) and image-entropy-based al-
gorithm (IEBA). A second-order polynomial phase model is
often used as the parametric model for motion compensa-
tion in algorithms such as the ICBA and the IEBA. Often
suchamodeldoesnotprovetobeaccurateenough,due
to irregular target motions, such as in the cases of fast ma-
noeuvring targets or sea-driven target angular motions in
rough sea surface conditions. Motivated by this, researchers,
such as those of the Martorella et al. paper, are employ-
ing high-order polynomial phase models to achieve accu-
rate image focussing. However, estimation of the required
polynomial coefficients (via solving of an optimisation prob-
lem) is typically sensitive to the cost function (image contrast
or entropy) and the iterative-search technique employed. In
particular, solutions provided by classic iterative techniques,
such as Newton, quasi-Newton, steepest descent, or gradient,
are generally unsuitable due to the multimodal characteris-
tics of the cost function (which become more severe as the
number of polynomial coefficients increases). To avoid such
convergence problems Martorella et al. consider a genetic-
based iterative technique, which they apply to the estima-
tion/optimisation of a third-order polynomial phase model.
The second paper, written by Yau et al., also addresses the
multimodal-related convergence difficulties associated with
many parametric-based motion compensation approaches.
This paper proposes to overcome the difficulties by decou-
pling the estimation of the first- and higher-order polyno-
mial coefficients. This is accomplished via an iterative two-
stage approach; first a range-profile cross-correlation step
is applied to estimate the first-order coefficient, and then a
subspace-based technique, involving eigenvalue decomposi-
tion (EVD) or singular value decomposition (SVD), is ap-
plied to estimate the higher-order coefficients. The potential
benefits of this two-stage approach arise because the optimi-
sation process is implemented over two lower-dimensional
spaces, thereby enhancing the likelihood of convergence to a
globally optimal solution.
Image formation
After motion compensation, the received signal is processed
to form the ISAR image. The classic way of forming an ISAR
image involves a two-step process. The first step concerns
the range compression (or range focussing). Here, either the
received time-domain signals are compressed by means of
matched filters or the received multifrequency signals are
compressed via the inverse Fourier transform—to produce
complex range profiles. It is worth pointing out that in some
cases the range compression is achieved before the motion
compensation. The second step consists of cross-range com-
pression (azimuth compression). The fastest and simplest
way of obtaining cross-range compression is by means of
a Fourier transform. In ISAR scenarios, where the target is
moving smoothly with respect to the radar and when the
integration time is short enough, the Fourier transform rep-
resents the most effective solution. Nevertheless, in ISAR sce-
narios with fast manoeuvring targets or sea-driven motioned
ships or with the requirement of high resolution, the ef-
fectiveness of the Fourier approach is strongly limited. For
this reason, several other techniques have been proposed in
the last decades, such as the JTFA, the range-instantaneous-
Doppler (RID), the enhanced image processing (EIP) tech-
niques, tomography-based techniques and super-resolution
techniques, such as the CLEAN technique, and the Capon
technique among others.
In this special issue, the paper by Djurovic et al. pro-
poses a novel image formation (cross-range compression)
technique based on the use of the polynomial Fourier trans-
form (PFT) for enhancing the ISAR image quality in complex
reflector geometries at a relatively low computational cost. A
model is introduced that describes the received signal as the
superposition of contributions from different geometrical ar-
eas with given characteristics in terms of signal phases. The
local polynomial Fourier transform (LPFT) is then used to
match the signal contributions that come from different im-
age areas.
The second paper on image formation, by Wong et al.,
proposes a method of analysis for quantifying the image dis-
tortion introduced by the conventional Fourier transform
approach. This analysis method involves a numerical model
of the time-varying target rotation rate. The analysis implies
that severe distortion is often attributed to phase modula-
tion effects, whereas a time-varying Doppler frequency pro-
duces image smearing. Following insights gained from the
analysis, the authors also propose a time-frequency process-
ing/analysis based method for deblurring/refocusing conven-
tionally generated ISAR images.
Target classification and identification
Radar signatures are often used for target classification
and/or identification. The need for classifying a target has
led to the development of high-resolution radar. ISAR im-
ages can be interpreted as two-dimensional (2D) radar sig-
natures. Therefore, a 2D distribution of the energy backscat-
tered from the target provides a multidimensional way of in-
terpreting the information carried by the radar echo. Sev-
eral techniques have been proposed for interpreting this
ISAR-based information for the purpose of target classifica-
tion/identification. These fall into two main philosophies: (i)
feature matching and (ii) template or point matching, the lat-
ter being more oriented towards target identification.

Marco Martorella et al. 3
In this special issue, two papers deal with the problem of
target classification by means of ISAR images. In the paper of
Shreyamsha Kumar et al., a full system for target identifica-
tion is proposed. The authors introduce a wavelet-based ap-
proach for ISAR image formation followed by feature extrac-
tion and target identification by means of neural networks.
The use of the wavelet technique is compared with time-
frequency techniques in terms of effectiveness and compu-
tational cost. In ISAR imaging it is sometimes difficult to
predict the target orientation and often even more difficult
to rescale the image along the cross-range coordinate. This
problem is avoided in the proposed technique as the features
used for target identification are invariant to translation, ro-
tation, and scaling—leading to a robust ISAR image-based
identification system.
ThesecondpaperbyRadoietal.proposesasuper-
vised self-organising feature-based classification technique
of super-resolution ISAR images. The super-resolution ISAR
images are obtained through a MUSIC-2D method, cou-
pled with phase unwrapping and symmetry enhancement.
The proposed feature vector contains Fourier descriptors
and moment invariants, which are extracted from the target
shape and scattering center distribution of the ISAR image.
These features, importantly, are invariant to target position
and orientation. The feature-based classification is then car-
ried out via a supervised adaptive resonance theory (SART)
approach, which shows improved efficiency over the conven-
tional MLP and fuzzy KNN classifiers.
Marco Martorella
John Homer
James Palmer
Victor Chen
Fabrizio Berizzi
Brad Littleton
Dennis Longstaff
Marco Martorella wasborninPortofer-
raio (Italy) in June 1973. He received
the Telecommunication Engineering Lau-
rea and Ph.D. degrees from the University
of Pisa (Italy) in 1999 and 2003, respec-
tively. He became a postdoc. Researcher in
2003 and a permanent Researcher/Lecturer
in 2005 at the Department of Information
Engineering of the University of Pisa. He
joined the Department of Electrical and
Electronic Engineering (EEE) of the University of Melbourne dur-
ing his Ph.D., the Department of Electrical and Electronic Engi-
neering (EEE) of the University of Adelaide under a postdoc. con-
tract, and the Department of Information Technology and Electri-
cal Engineering (ITEE) of the University of Queensland as a Vis-
iting Researcher between 2001 and 2006. His research interests are
in the field of synthetic aperture radar (SAR) and inverse synthetic
aperture radar (ISAR). He is an IEEE Member since 1999.
John Homer received the B.S. degree in
physics from the University of Newcastle,
Australia in 1985 and the Ph.D. degree in
systems engineering from the Australian
National University, Australia, in 1995. Be-
tween his B.S. and Ph.D. studies, he held
a position of Research Engineer at Coma-
lco Research Centre in Melbourne, Aus-
tralia. Following his Ph.D. studies, he has
held research positions with the University
of Queensland, Veritas DGC Pty Ltd., and Katholieke Universiteit,
Leuven, Belgium. He is currently a Senior Lecturer at the Univer-
sity of Queensland within the School of Information Technology
and Electrical Engineering. His research interests include signal and
image processing, particularly in the application areas of telecom-
munications, audio and radar. He is currently an Associate Editor
of the Journal of Applied Signal Processing.
James Palmer was born in 1979 in Towns-
ville, Australia. James received the Bachelor
of electrical engineering (Hons I) and Bach-
elor of Arts (Japanese) degrees from the
University of Queensland and is currently
finishing his Ph.D. studies through the same
institution. Palmer’s major research inter-
ests are in the field of bistatic radar, SAR and
ISAR (including the monostatic, emulated
bistatic, and bistatic varieties), and sea sur-
face forward scatter RF signal modelling and analysis.
Victor Chen received the Ph.D. degree in
electrical engineering from Case Western
Reserve University, Cleveland, Ohio, in
1989. Since 1990, he has been with Radar
Division, the US Naval Research Labora-
tory in Washington DC and working on ra-
dar imaging, time-frequency applications to
radar, ground moving target indication, and
micro-Doppler analysis. He is a Principal
Investigator working on various research
projects on radar signal and imaging, time-frequency applications
to radar, and radar micro-Doppler effect. He served as Technical
Program Committee Member and Session Chair for IEEE and SPIE
conferences and served as a Guest Editor for IEE Proceedings on
Radar, Sonar, and Navigation in 2003, and Associate Editor for the
IEEE Trans. on Aerospace & Electronic Systems since 2004. His
current research interests include computational synthetic aperture
radar imaging algorithms, micro-Doppler radar, and independent
component analysis of features for noncooperative target identifi-
cation. He received NRL Review Award in 1998, NRL Alan Berman
Research Publication award in 2000 and 2004, and NRL Techni-
cal Transfer Award in 2002. He has more than 100 publications in
books, journals, and proceedings including a book: Time-Frequency
Transforms for Radar Imaging and Signal Analysis (V.C.Chenand
Hao Ling), Artech House, Boston, Mass, January 2002.
Fabrizio Berizzi was born in Piombino
(Italy) on November 1965. He received the
Electronic Engineering and Ph.D. degrees
from the University of Pisa (Italy) in 1990
and 1994, respectively. Currently, he is an
Associate Professor of the University of Pisa
(Italy)—Department of Information Engi-
neering. His main research interests are in
the fields of synthetic aperture radar (SAR
and ISAR), HF-OTH skywave and surface

4 EURASIP Journal on Applied Signal Processing
wave radar, target classification by wideband polarimetric radar
data, hybrid waveform design for HRRP radar. He is the author and
coauthor of more than 100 papers published in prestigious interna-
tional journals, book chapters, and IEEE conference proceedings.
He is the principal investigator of several research projects funded
by Italian radar industries and by the Italian Minister of Defense.
He cooperates to several research activities with the University of
Adelaide (AUS), DSTO (AUS), JPL (USA), NRL (USA), ONERA
(France), SOC (UK). He is a Member of the IEEE.
Brad Littleton received his Ph.D. in physics
from the University of Queensland, in 2004.
His research interests are elastic and in-
elastic electromagnetic wave/matter inter-
actions, and applications to electromagnetic
imaging, measurement and superresolution
techniques. He is currently working on sin-
gle quantum dot spectroscopy for the UQ
node of the Centre for Quantum Computer
Technology.
Dennis Longstaffis currently Technology
Consultant to Filtronic PLC and Emeri-
tus Professor with the School of Informa-
tion Technology and Electrical Engineer-
ing at the University of Queensland. Dur-
ing that time at the University of Queens-
land, Dennis cofounded the Cooperative
Research Centre for Sensor Signal and In-
formation Processing (CSSIP). He was also
the Founder and Director of GroundProbe,
now a thriving global company marketing products invented by
him and developed by his research group. He also served as Head
of Department of Electrical and Computer Engineering for three
years. From 1988 to 1991, he was at the Defence Science and Tech-
nology Organisation (DSTO) in Australia, where he was Research
Leader to the Microwave Radar Division in Adelaide. Previous to
this he spent 18 years as Senior Scientific Officer, then Principal Sci-
entific Officer at the Royal Signals and Radar Establishment (now
QintiQ), Malvern, England, where he worked on airborne radar
systems. His work has attracted a number of awards and prizes and
his spinoffcompany, GroundProbe, received an Engineering Excel-
lence Award from the IE(Aust) Qld 2003. He was granted a Queens-
land Government Smart State Award in 2004, and an Australian
Emerging Exporter Award in 2005 (see www.groundprobe.com).

Photographȱ©ȱTurismeȱdeȱBarcelonaȱ/ȱJ.ȱTrullàs
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