The following is a representative description of AIMS' government
and commercial contracts and the areas of work involved.
National Institutes of Health/National Cancer
Electrical Impedance Tomography
Electrical Impedance Tomography, or EIT, is a method of imaging human tissue
based on tissue conductivity, as opposed to density which is the basis
for ultrasound and X-ray methods. In EIT, a belt of simple electrodes is
placed around the body or a limb. This ring of sensors provides input signal
patterns that can be processed by a desktop computer to yield a grey-scale
image of an interior cross-section in which ``light" and ``dark" correspond
to ``more conductive" and ``less conductive." There are numerous instances
within the human body where adjacent soft tissues of similar density possess
markedly different conductivities. EIT is not necessarily confined to use
involving humans, but is also suitable for ``imaging" the interior of any
object, a suspicious package for example. For 3-dimensional imaging, the
single ring of sensors can be replaced by a band of vertically stacked
AIMS scientists have discovered a new technique to process the
electrode data which represents a quantum leap in the theoretical understanding
and practical implementation of EIT. This technique leads to EIT imagery
of heretofore unachievable resolution. The National Institutes of Health
have recently recognized this by awarding AIMS with a Small Business
Innovative Research grant to develop the technology. The most attractive
features of EIT technology are the following:
Low-cost: Comparable in cost to ultrasound imaging.
Non-invasive: No dangerous X-rays or radioactive tracers.
Easy operation: Equipment is safe, portable and simple to use.
Real-time imaging: Add-on boards permit real-time diagnostic ``movies,"
e.g., cardio-pulmonary function.
High-resolution: First generation EIT has resolution comparable
to current ultrasound technology.
Tomographic: Choice of cross-sectional ``slices" or full 3D images
inside the body or limbs.
Tissue mapping: Unique dielectric operation images internal organs
by their electro-chemical composition, not simply by tissue density, as
with X-rays and ultrasound. This has important applications in medicine
as well as criminal investigation and non-destructive testing of materials
and structures for faults.
Figure 1: A schematic of AIMS' EIT imaging system
showing how a 16-electrode configuration is used to reconstruct a ``slice"
of an internal conductivity distribution.
Figure 2: Simulated image reconstructions comparing the
X-ray transform and the new EIT algorithm for a 16-electrode imaging system.
Advanced Research Projects Agency (ARPA)
Wavelet and Learning Clustering Algorithms for
Automatic Target Recognition
In this effort, AIMS continues to be a leading innovator in the
area of automated target recognition. A fundamental difficulty of target
recognition is to develop a unified methodology for target feature representation,
extraction, and classification. This project achieves such a unification
in a hierarchical way that takes advantage of the localization properties
of the wavelet transform to generate multi-scale representations of target
features. The proposed method may be called progressive recognition,
in which the output of wavelet preprocessing is used as input to learning
clustering algorithms which in turn provide feedback to the wavelet preprocessor.
Anticipated applications of this method are:
Compression, storage and progressive retrieval of FLIR, LADAR and visual
target signatures, as well as high resolution radar returns for use in
smart, autonomous reconnaissance, surveillance and target acquisition systems.
Ground vehicle acoustic signature representation and compression.
Early fault and damage detection in gear-trains, machinery and other equipment.
Recognition of industrial objects in manufacturing.
Underwater passive sonar target recognition systems.
Large image database reduction and fast matching, browsing and retrieval.
Figure 3: Graphical interface to AIMS' wavelet transform
software, being used here to develop image compression algorithms for fingerprint
storage and transmission.
Center for Night Vision/ US Army CECOM
Automatic Target Recognition
This project was a major one-year effort by AIMS that resulted in
several advances in the state of the art of automated target recognition
(ATR). The primary thrust was to perform basic research and development
for next-generation, model-based target recognition systems. AIMS'
methodology and algorithms have demonstrated superior performance in several
controlled, blind tests. Specific innovations include:
Model Construction: Model building techniques that take in CAD models,
field imagery or a combination of the two with equal ease.
Aspect VQ: Procedures, based on vector quantization, to systematically
identify the minimal aspect representatives of a given target class reqeuired
in a model database to achieve a desired level of performance.
Multiresolution Polygonalization: Algorithms that allow one to compute,
store and index polygonal models of target silhouettes or signatures as
a function of range (or other) resolution. This provides a foundation to
develop algorithms that employ progressive recognition.
Probeset Likelihood Ratios: We have extended the probing concepts
of computational geometry with a general definition of likelihood for probeset
On-line Optimal Probing: We have implemented a number of new, grey-scale
probing methods for viewpoint estimation, target class decisions and clutter
rejection. We have also introduced new techniques that permit the on-line,
real-time computation of the optimal probe sets for between-class discrimination.
Figure 4: FLIR image of 4 M-60 tanks at 1.5 km correctly
recognized by AIMS Probing Algorithm.
Probe Extensions: Alternative sensors, imaging or otherwise, induce
probesets that can be handled by our methods in a natural way. In particular,
our definition of probeset likelihood is sensor independent.
Model Extensions: We have shown how the polygonalization methods
can be adapted to any one-dimensional signal, and for example, have pointed
out the similarities that exist between polygonal signatures and mmWave
Learning: Since our off-line model/parameter tuning methods are
based on general vector quantization theory, our methods extend naturally
to LADAR, TV, mmWave, SAR, i.e., any signal space.
Multi-look: Due to the set theoretic formulation of the optimal
probeset notion, extending the algorithm to include multiple views of a
single target is simple.
ATR Metrology Standards:
Moving Targets: Our notion of probeset extends immediately to the
case where the targets are in motion since it induces a natural definition
of state that allows the direct insertion of standard filtering methods
such as Kalman or extended Kalman filtering, or nonlinear filtering.
Moving Sensors: Again, the extension is clear-cut and can be handled
in a natural way using a Lagrangian formulation for the dynamics.
Algorithm Quality: We have clearly delineated the inadequacies of
the metrics currently used to assess the quality of ATR algorithms and
have defined the notion of relative PEC statistics (Performance-Efficiency-Complexity)
as a remedy.
Database Difficulty: We have also outlined a method that permits
the direct comparison of ATR databases and to rate their relative difficulty.
Figure 5: Graphical interface to AIMS ATR Probing
Algorithm used for algorithm visualization.
Naval Research Laboratory
Algorithms and Software Systems for Sensor Integration
Because future ship sensor systems are facing increasingly complex and
dynamic environments, there is a critical need to develop advanced technology
for next generation shipboard systems. A cornerstone of such technology
is the development of efficient multi-sensor multi-target tracking systems
which are able to function on board ships and operate as part of a large
network which includes other ships or platforms equipped with similar systems.
AIMS engineers are currently developing algorithms and software
systems for sensor integration. Development of a sensor fusion testbed
is currently underway on high-end graphics Silicon Graphics workstations.
The figure below illustrates the sensor integration interface and its implementation
of a ``bearings only'' tracking algorithm.
Figure 6: Tracking algorithm based on multi-sensor input
implemented in graphical software system developed by AIMS engineers.
Naval Research Laboratory Specific Emitter Identification
The main objective of this on-going project is to provide a passive method
of identifying specific radar emitters based soley on measurement of the
pulses which they emit.
Passive identification of radar emitters in emitter dense and electromagnetically
cluttered environments is a technology which would find both commercial
and military applicability. In particular, the ability to identify radar
emitters based solely on the passive measurement of pulses which they emit
provides a basis for at least three major areas of direct benefit: (i)
target recognition/combat id, (ii) target tracking, (iii) global and tactical
situational awareness. In a real battle scenario in which there are mobile
radar emitting targets such as ships, aircraft, and radar guided missile
platforms any one of these capabilities is essential. Moreover, such technology
may be employed in both offensive and defensive weapon systems. In a defensive
role a passive emitter identification technology could provide automatic
and accurate warnings of threats and potential danger. In an offensive
role, a passive emitter identification technology could be used as a key
component in a missile guidance system intended to neutralize radar emitting
Figure 7: Schematic of a pulse classification system.
A crucial issue which is being addressed at AIMS is the development,
analysis, and comparison of representations of emitter signals. Particular
emphasis is being placed on determining representations which uniquely
and robustly characterize emitter pulse behavior in the face of noise,
clutter, and parameter agility. To this end, AIMS engineers have
been developing and testing wavelet and wavelet-like representations of
Another large focus of the effort concerns the development of learning
algorithms. An important requirement for an overall pulse recognition system
is that it be endowed with the ability to ``learn'' fast and efficiently.
In terms of learning, the fundamental specification is to learn, by example,
the defining characteristics of a particular radar.
AIMS' approach to SEI may be extended to problems of generic
pattern recognition. Potential areas include speech recognition, speaker
identification, face recognition, finger print identification, and the
recognition of tumors and growths in medical image processing applications.
Figure 8: Wavelet processing software developed for use
Naval Research Laboratory / Department of the
Electronic Countermeasures Effectiveness Monitoring
This ongoing series of projects involves another aspect of ship self-defense,
namely the lethality assessment of potential threats, and the monitoring
of the effectiveness of countermeasures taken against these threats. Straightforward
solutions to these problems are confounded by the adverse affects of radar
multipath. AIMS engineers have developed fast regularization filtering
and advanced signal processing techniques that demonstrate significant
promise in combatting these problems. Ongoing efforts in this area include
software development for a graphical, sensor-fusion simulation testbed,
as well as more advanced algorithms for multipath cancellation.
Figure 9: AIMS developed new processing techniques
that significantly enhance signal subspace methods applied to direction
of arrival estimation in the presence of multipath
Figure 10: This sequence of figures shows the steps involved
in estimating the look-angle of an anti-ship cruise missile. The accuracy
of the estimated (dotted) compared to the true waveforms was considered
unobtainable prior to AIMS' ongoing effort.
Naval Research Laboratory / Department of the
Active Radar Decoy Performance Evaluation
This project is one of several AIMS projects related to the important
problem of ship self-defense.
Navy's electronic warfare community has demonstrated a need for low
speed, low altitude, small unmanned aerial vehicles (UAVs) for use as ship
decoys. UAVs that are launched from a container on a ship's deck and that
carry electronic payloads away from the ship's location can provide highly
effective offboard countermeasures against hostile radars and radar-guided
weapons. By creating realistic appearing radar images of the ship, these
UAVs can decoy the incoming missiles.
Figure 11: The FLYing Radar Target (FLYRT) design configuration
(Figure reproduced from NRL Publication 217-4810.)
AIMS has been tasked with a multiyear effort to evaluate the
fidelity with which these decoys mimic the ship and hence confuse the threat.
AIMS' proprietary tools, e.g., the Learning Vector Quantization
(LVQ) classifier, have been used as the benchmark discriminator. Considering
ever increasingly complicated threat scenarios in which missiles may employ
pulse to pulse frequency agility or integration schemes, AIMS engineers
are devising experiments to assess decoy effectiveness as well as to determine
additional degrees of freedom which may need to be built into decoy designs.
Naval Research Laboratory / Department of the
Modeling Support for Unconventional Techniques
for Anti-Ship Missile Defense
A difficult threat for ship EW systems is the antiship cruise missile (ASCM).
Typically, such threats fly at a low altitude and with low probability-of-intercept
(LPI) emission signatures. This often results in late detection and little
time to respond. This effort investigates a new defense technique which
attacks a key ASCM sensor.
Figure 12: AIMS has introduced the powerful methodology
of control system synthesis and analysis under energy and observability
constraints to this important ECM problem.
As part of this effort, AIMS has been tasked by the Navy to develop
distributed sea clutter and multipath models for radar simulation. As opposed
to the point scattering models these distributed scatterer models have
been demonstrated to be more realistic. Based on sound principles in electromagnetics,
these models also employ stochastic features to emulate the temporal characteristics
of monostatic as well as bistatic backscatter from ocean surfaces. Working
code has been delivered to the Navy, and preparation of a software package
is in progress which shall operate with the accepted nomenclature of environmental
parameters such as wind speed and sea states.
National Security Agency
CELP Speech Compression
With the advent of multimedia communications and the continuing integration
of communications networks with computers, software, and databases, the
compression of voice data is crucial for these rapidly advancing technologies
not to be overwhelmed by the volume of the data involved. Dr. J.S. Baras,
Chief Scientist of AIMS, has co-invented a record-breaking, real-time
speech-coding algorithm (patent pending) that achieves toll-quality speech
reproduction at a rate of 4.8 kilobits/second with below 5 MIPS complexity,
which is 25% of the complexity of current methods having comparable fidelity.
AIMS engineers are at work with the National Security Agency on
improvements to the algorithm and a prototype hardware implementation using
low-cost digital signal processing chips. The potential applications for
this product are far-reaching and include the following:
Raster-to-Vector Document Conversion
The vast majority of the world's existing collection of engineering schematics,
architectural blueprints, scientific diagrams and maps are only available
on paper. At the same time, there is an exponentially increasing trend
away from hardcopy formats that is to a large extent due to the widespread
use of powerful workstations and software for Computer-Aided Design (CAD).
As a consequence, there is a pressing requirement to transfer hardcopy
graphics data to magnetic media and to do so using data formats which are
readable by CAD software programs.
Digital answering machines.
Compressed storage of audio ``help files" for software annotation.
The purpose of raster-to-vector conversion is to make the transfer of
drawings from paper to CAD-suitable formats. This means that the data must
be transferred as ``vectorized" collections of geometric objects (lines,
circles, etc.) and character data rather than simply binarized or grey-scale
raster images. This type of conversion will make the converted drawings
usable by CAD, database, and data transmission software, and will thus
make the conversion more than an archiving process, but a true transition
of the drawings to the CAD environment.
AIMS has identified a number of technical areas in which we can
make significant contributions to the raster-to-vector conversion problem.
We offer superior filtering algorithms which can greatly enhance the conversion
process. These include both weak continuity and morphological multiresolution
filtering. AIMS engineers have also developed fast algorithms for
the creation of general masks, using parameterized curves, B-splines and
combinatorial composition techniques, as well as new, fast algorithms for
zooming and resizing images. We also have the latest algorithms for character
and geometrical object recognition in highly noisy environments which substantially
augment or supersede the Hough Transform.