This paper provides a summary of recent results on a novel multi-platform RF emitter localization technique denoted as
Position-Adaptive RF Direction Finding (PADF). This basic PADF formulation is based on the investigation of iterative
path-loss based (i.e. path loss exponent) metrics estimates that are measured across multiple platforms in order to
robotically/intelligently adapt (i.e. self-adjust) the location of each distributed/cooperative platform. Recent results at the
AFRL indicate that this position-adaptive approach shows potential for accurate emitter localization in challenging
embedded multipath environments (i.e., urban environments). As part of a general introductory discussion on PADF
techniques, this paper provides a summary of our recent results on PADF and includes a discussion on the underlying
and enabling concepts that provide potential enhancements in RF localization accuracy in challenging environments.
Also, an outline of recent results that incorporate sample approaches to real-time multi-platform data pruning is included
as part of a discussion on potential approaches to refining a basic PADF technique in order to integrate and perform
distributed self-sensitivity and self-consistency analysis as part of a PADF technique with distributed robotic/intelligent
features. The focus of this paper is on the experimental performance analysis of hardware-simulated PADF
environments that generate multiple simultaneous mode-adaptive scattering trends. We cite approaches to addressing
PADF localization performance challenges in these multi-modal complex laboratory simulated environments via
providing analysis of our multimodal experiment design together with analysis of the resulting hardware-simulated
This paper provides a summary of preliminary RF direction finding results generated within an AFOSR funded testbed
facility recently developed at Louisiana Tech University. This facility, denoted as the Louisiana Tech University Micro-
Aerial Vehicle/Wireless Sensor Network (MAVSeN) Laboratory, has recently acquired a number of state-of-the-art
MAV platforms that enable us to analyze, design, and test some of our recent results in the area of multiplatform
position-adaptive direction finding (PADF)   for localization of RF emitters in challenging embedded multipath
environments. Discussions within the segmented sections of this paper include a description of the MAVSeN Laboratory
and the preliminary results from the implementation of mobile platforms with the PADF algorithm. This novel approach
to multi-platform RF direction finding is based on the investigation of iterative path-loss based (i.e. path loss exponent)
metrics estimates that are measured across multiple platforms in order to develop a control law that
robotically/intelligently positionally adapt (i.e. self-adjust) the location of each distributed/cooperative platform. The
body of this paper provides a summary of our recent results on PADF and includes a discussion on state-of-the-art
Sensor Mote Technologies as applied towards the development of sensor-integrated caged-MAV platform for PADF
applications. Also, a discussion of recent experimental results that incorporate sample approaches to real-time singleplatform
data pruning is included as part of a discussion on potential approaches to refining a basic PADF technique in
order to integrate and perform distributed self-sensitivity and self-consistency analysis as part of a PADF technique with
distributed robotic/intelligent features. These techniques are extracted in analytical form from a parallel study denoted as
"PADF RF Localization Criteria for Multi-Model Scattering Environments". The focus here is on developing and
reporting specific approaches to self-sensitivity and self-consistency within this experimental PADF framework via the
exploitation of specific single-agent caged-MAV trajectories that are unique to this experiment set.
Uncertainty plays decisive role in the confidence of the decisions made about events. For example, in situation awareness,
decision-making is faced with two types of uncertainties; information uncertainty and data uncertainty. Data uncertainty
exists due to noise in sensor measurements and is classified as <i>randomness</i>. Information uncertainty is due to ambiguity of
using (words) to describe events. This uncertainty is known as <i>fuzziness</i>. Typically, these two types of uncertainties are
handled separately using two different theories. Randomness is modeled by probability theory, while fuzzy-logic is used to
address fuzziness. In this paper we used the Cloud computation theory to treat data randomness and information fuzziness in
one single model. First, we described the Cloud theory then used the theory to generate one and two-dimensional Cloud
models. Second, we used the Cloud models to capture and process data randomness and fuzziness in information relative to
decision-making in situation awareness. Finally, we applied the models to generate security decisions for security monitoring
of sensitive area. Testing results are reported at the end of the paper.
We discuss the development, design, implementation, and demonstration of a robotic UGV (Unmanned Ground Vehicle)
system for networked and non-line-of-sight sensing applications. Our development team is comprised of AFRL Summer
Interns, University Faculty, and Personnel from AFRL. The system concept is based on a previously published technique
known as "Dual-UAV Tandems for Indirect Operator-Assisted Control" . This architecture is based on simulating a
Mini-UAV Helicopter with a building-mounted camera and simulating a low-flying QuadRotor Helicopter with a
Robotics UGV. The Robotics UGV is fitted with a custom-designed sensor boom and a surrogate chem/bio (Carbon
Monoxide) PCB sensor extracted from a COTS (Commercial-Off-The-Shelf) product. The CO Sensor apparatus is co-designed
with the sensor boom and is fitted with a transparent covering for protection and to promote CO (surrogate
chem/bio) flow onto the sensor.
The philosophy behind this non-line-of-sight system is to relay video of the UGV to an Operator station for purposes of
investigating "Indirect Operator-Assisted Control" of the UGV via observation of the relayed EO video at the operator
station. This would serve as a sensor fusion, giving the operator visual cues of the chemical under detection, enabling
him to position the UGV in areas of higher concentration. We recorded this data, and analyzed the best approach given a
test matrix of multiple scenarios, with the goal of determining the feasibility of using this layered sensing approach and
the system accuracy in open field tests.
For purposes of collecting scientific data with this system, we developed a Test (data collection) Matrix with following
three parameters: 1. Chem/Bio detection level with side-looking sensor boom and slowly traversing UGV; 2. Chem/Bio
detection level with panning sensor boom and slowly traversing UGV; 3. Chem/Bio detection level with forward-looking
sensor boom and operator-assisted steering based on onboard wind vane readings of UGV display that is
overlayed onto relayed video. In addition to reporting the trends and results of analysis with regard to data collected with
this Test Matrix, we discuss potential approaches to upgrading our networked robotics UGV system and also introduce
the concept of "swapping sensors" with this low-cost networked sensor concept.
This paper discusses some potential approaches to developing next-generation radar technologies via the development of
smart hardware subsystems that have the intrinsic capability to compensate for channel warping and propagation
distortions. Discussions with regard to typical categories of propagation distortions are provided along with a series of
sample scenarios that can provide challenging conditions for radar remote sensing applications. Observations of this set
of sample scenarios allows for the exploration of potential approaches to developing state-of-the art digital, RF, and
RF/Photonic technologies with intrinsic smart compensation capabilities that will enable the development of highresolution
radars that can operate at longer ranges while maintaining compact size and weight requirements.
An alternative approach to a Layered Sensing System-of-Systems methodology, denoted as LSWR (Layered Sensing
With Radio), is outlined in this paper. This is a novel Broadcast-TV-Driven layered sensing technique that shows
potential for finding embedded objects within, for example, buildings via leveraging and combining existing commercial
satellite technologies with COTS (Commercial Off-the-Shelf) wireless network technologies and state-of-the-art wireless
sensor mote technologies. Specifically, compact sensor mote technologies are employed in a cost-effective manner to
interface with and control low-cost satellite radio/broadcast tuners. With this approach, initial concepts of this type are
investigated via the analysis of compact custom sensor node technology (i.e. wireless sensor mote interfaced with
satellite broadcast tuner) integrated onto a UGV (unmanned ground vehicle) robot arm for purposes developing
prototype UGV robot systems with passive integrated RF sensors that support, for example, networked thru-wall
embedded object detection. The primary category of commercial satellite signal considered for analysis within this
paper is known as DVB (Digital Video Broadcast).
As part of the Student Internship Programs at Wright-Patterson Air Force Base, including the AFRL Wright Scholar
Program for High School Students and the AFRL STEP Program, sample results from preliminary investigation and
analysis of integrated antenna structures are reported. Investigation of these novel integrated antenna geometries can be
interpreted as a continuation of systems analysis under the general topic area of potential integrated apertures for future
software radar/radio solutions  . Specifically, the categories of novel integrated aperture geometries investigated in
this paper include slotted-fractal structures on microstrip rectangular patch antenna models in tandem with the analysis
of exotic substrate materials comprised of a type of synthesized electromagnetic structure known as metamaterials  -
A number of potential advantages associated with a new concept denoted as Sensor Agnostic Networks are discussed.
For this particular paper, the primary focus is on integrated wireless networks that contain one or more MAVs (Micro
Unmanned Aerial Vehicle). The development and presentation includes several approaches to analysis and design of
Sensor Agnostic Networks based on the assumption of canonically structured architectures that are comprised of lowcost
wireless sensor node technologies. A logical development is provided that motivates the potential adaptation of
distributed low-cost sensor networks that leverage state-of-the-art wireless technologies and are specifically designed
with pre-determined hooks, or facets, in-place that allow for quick and efficient sensor swaps between cost-low RF
Sensors, EO Sensors, and Chem/Bio Sensors. All of the sample design synthesis procedures provided within this paper
conform to the structural low-cost electronic wireless network architectural constraints adopted for our new approach to
generalized sensing applications via the conscious integration of Sensor Agnostic capabilities.
This paper presents a distributed multi-modality sensor network concept for vehicle classification within perimeter of a
surveillance system. This perimeter surveillance concept represents a "Virtual RF Fence" consisting of remotely
located electro-optic surveillance cameras and a standoff range radar system. The perimeter surveillance system
vigilantly monitors the field and each time a vehicle crosses the virtual RF fence it informs the surveillance cameras to
actively monitor the activity of vehicles as it passes through the field. This paper describes the methodologies applied
for processing the EO imagery data including target vehicle segmentation from background, vehicle shadow
elimination, vehicle feature vector generation, and a neural network approach for vehicle classification. A metric is also
proposed for evaluation of performance of the vehicle classification technique.
We discuss the development of Position-Adaptive Sensors  for purposes for detecting embedded chemical substances
in challenging environments. This concept is a generalization of patented Position-Adaptive Radar Concepts developed
at AFRL for challenging conditions such as urban environments. For purposes of investigating the detection of
chemical substances using multiple MAV (Micro-UAV) platforms, we have designed and implemented an experimental
testbed with sample structures such as wooden carts that contain controlled leakage points. Under this general concept,
some of the members of a MAV swarm can serve as external position-adaptive "transmitters" by blowing air over the
cart and some of the members of a MAV swarm can serve as external position-adaptive "receivers" that are equipped
with chemical or biological (chem/bio) sensors that function as "electronic noses". The objective can be defined as
improving the particle count of chem/bio concentrations that impinge on a MAV-based position-adaptive sensor that
surrounds a chemical repository, such as a cart, via the development of intelligent position-adaptive control algorithms.
The overall effect is to improve the detection and false-alarm statistics of the overall system.
Within the major sections of this paper, we discuss a number of different aspects of developing our initial MAV-Based
Sensor Testbed. This testbed includes blowers to simulate position-adaptive excitations and a MAV from Draganfly
Innovations Inc. with stable design modifications to accommodate our chem/bio sensor boom design. We include details
with respect to several critical phases of the development effort including development of the wireless sensor network
and experimental apparatus, development of the stable sensor boom for the MAV, integration of chem/bio sensors and
sensor node onto the MAV and boom, development of position-adaptive control algorithms and initial tests at IDCAST
(Institute for the Development and Commercialization of Advanced Sensor Technologies), and autonomous positionadaptive
chem/bio tests and demos in the MAV Lab at AFRL Air Vehicles Directorate. For this particular MAV
implementation of chem/bio sensors, we selected miniature Methane, Nitrogen Dioxide, and Carbon Monoxide sensors.
To safely simulate the behavior of chem/bio substances in our laboratory environment, we used either cigarette smoke
or incense. We present a set of concise parametric results along with visual demonstration of our new position-adaptive
sensor capability. Two types of experiments were conducted: with sensor nodes screening the chemical contaminant
(cigarette smoke or incense) without MAVs, and with a sensor node integrated with the MAV. It was shown that the
MOS-based chemical sensors could be used for chemical leakage detection, as well as for position-adaptive sensors on
air/ground vehicles as sniffers for chemical contaminants.
This paper presents three different types of ultra-wideband (UWB) antennas for RF micro-radar applications in range of
1-3 GHz and discusses performance characteristics of each antenna. The electromagnetic radiation properties of
antennas are analyzed based on their gain, bandwidth, voltage standing wave ratio (VSWR), conductivity, and form and
size factors. In addition, the performance of each antenna is investigated with different feed line configuration to
determine influence of feed line configuration upon the performance of each UWB antenna. The antennas were modeled
using electromagnetic simulation software, FEKO. The software simulates the performance of each antenna via Method
of Moment (MoM) technique that is a powerful method for analysis of electromagnetic radiation characteristics of RF
antennas. This paper presents the simulation-based performance comparison of the three selected UWB antennas under
the same operational bandwidth.
Position-Adaptive Radar concepts have been formulated and investigated at the AFRL within the past few years.
Adopting a position-adaptive approach to the design of distributed radar systems shows potential for the development of
future radar systems that function under a variety of new and challenging environments. Specifically, we investigate
notional control geometries and trajectories for multi-platform SUAV applications by integrating additional
electromagnetic scattering-based metrics within more generic overall objective functions for multi-SUAV controls
systems. We show that the formulation of these new categories of objective functions lead to realizations of multiplatform
SUAV trajectories that position adaptively converge to a set of RF leakage points. After position-adaptive
convergence to a set of leakage points, we show that an embedded scatterer (i.e. a metal cylinder) can be imaged by
applying radar processing techniques derived for sparse apertures.
Recent developments in communications and RF technology have enabled system concept formulations and designs
for low-cost radar systems using state-of-the-art software radio modules. One of the major benefits of using these
RF communications products is the potential for generating frequency-agile waveforms that are re-programmable in
real-time and potentially adapt to a scattering environment. In addition, recent simulation results  indicate that
this type of system enables the development and implementation of multi-function RF systems that yield good
performance within embedded shared-spectrum environments. This paper investigates the design and
implementation of software radar systems via implementation of commercially available software radio modules.
Specifically, the potential for developing alternative multi-tone radar systems that provide significant levels of
information with respect to embedded indoor scattering environments is discussed. This approach is developed via
the transform domain waveform synthesis/design and implementation of OFDM (Orthogonal Frequency Domain
Multiplexing) waveforms and shows good potential for the future development of cooperative multi-function RF
This paper addresses a number of design issues that are associated with integrating lightweight and low-cost RF (Radio
Frequency) sensors onto small UGV's (Unmanned Ground Vehicles) and UAV's (Unmanned Aerial Vehicles).
Modular\integrated RF sub-systems functions that are discussed include lightweight software programmable radar (or
software radar) using COTS software radio components and compact microstrip antenna design concepts for low-frequency
surface penetration radars. A discussion on the potential for implementing lightweight multi-function RF
systems as well as a discussion on novel futuristic concepts that explore the limits of sensor/platform integration is
We have formulated a series of position-adaptive sensor concepts for explosive detection applications using swarms of
micro-UAV's. These concepts are a generalization of position-adaptive radar concepts developed for challenging
conditions such as urban environments. For radar applications, this concept is developed with platforms within a
UAV swarm that spatially-adapt to signal leakage points on the perimeter of complex clutter environments to collect
information on embedded objects-of-interest.
The concept is generalized for additional sensors applications by, for example, considering a wooden cart that
contains explosives. We can formulate system-of-systems concepts for a swarm of micro-UAV's in an effort to detect
whether or not a given cart contains explosives. Under this new concept, some of the members of the UAV swarm can
serve as position-adaptive "transmitters" by blowing air over the cart and some of the members of the UAV swarm can
serve as position-adaptive "receivers" that are equipped with chem./bio sensors that function as "electronic noses". The
final objective can be defined as improving the particle count for the explosives in the air that surrounds a cart via
development of intelligent position-adaptive control algorithms in order to improve the detection and false-alarm
statistics. We report on recent simulation results with regard to designing optimal sensor placement for explosive or
other chemical agent detection. This type of information enables the development of intelligent control algorithms for
UAV swarm applications and is intended for the design of future system-of-systems with adaptive intelligence for
advanced surveillance of unknown regions. Results are reported as part of a parametric investigation where it is found
that the probability of contaminant detection depends on the air flow that carries contaminant particles, geometry of the
surrounding space, leakage areas, and other factors. We present a concept of position-adaptive detection (i.e. based on
the example in the previous paragraph) consisting of position-adaptive fluid actuators (fans) and position-adaptive
sensors. Based on these results, a preliminary analysis of sensor requirements for these fluid actuators and sensors is
presented for small-UAVs in a field-enabled explosive detection environment. The computational fluid dynamics (CFD)
simulation software Fluent is used to simulate the air flow in the corridor model containing a box with explosive
particles. It is found that such flow is turbulent with Reynolds number greater than 106. Simulation methods and results
are presented which show particle velocity and concentration distribution throughout the closed box. The results indicate
that the CFD-based method can be used for other sensor placement and deployment optimization problems. These
techniques and results can be applied towards the development of future system-of-system UAV swarms for defense,
homeland defense, and security applications.
Ultra-wideband (UWB) signals exhibit different characteristics upon propagation through matter compared with
narrowband signals. The latter keeping a sinusoidal shape during different forms of signal propagation. The behavior of
narrowband signals does not apply to UWB signals in many cases. Presently, the possibilities for development of UWB
signaling technology remain largely unexplored. Only a few applications have been developed due to strict regulations
by the Federal Communications Commission (FCC). In this paper we describe a series of experiments that have been
carried out to determine the behavior of UWB signals and their properties. A TEM horn antenna has been made for
radiating UWB signals. Experiments on pulse propagation have been carried out including an application to detection of
stationary metal objects. A high accuracy in detecting metal objects has been achieved. A procedure for propagating
UWB signals through a liquid medium of given salt concentration has been demonstrated, providing a basis for studying
UWB signal propagation in biological matter. A new pulsewidth definition was adopted which is suitable for UWB
This paper outlines a concept for exploiting UAV (Unmanned Aerial Vehicle) trajectories for detecting slowly moving
targets. All the analysis and simulation results are reported under the assumption of a circular UAV trajectory with
various degrees of localized perturbations in the neighborhood of a given circular trajectory. These trajectory
perturbations are introduced and investigated in order to develop intelligent processing algorithms for purposes of
detecting slowly moving targets. The basic concept is based on collecting sub-apertures of data over a given set of
localized trajectories and intelligently parsing the collected data based on time-varying angle estimates between the
localized UAV trajectory and subsets of a collection of moving point targets. This parsed data is intelligently combined
over large SAR integration sub-intervals and intervals to develop a novel approach to detecting moving targets with large
variations in speed and target trajectory. Simulation results are reported for three different trajectory perturbation
A description of the design parameters for a scaled RF environment is presented. This scaled RF environment was developed for purposes of simulating and investigating multipath phenomena in urban environments. A number of experiments were conducted with this scaled urban environment including a series of tests with eight spatially distributed receivers and one transmitter. Details with regard to the instrumentation system along with the measurement philosophy are provided. The primary focus of this paper is a detailed treatment of data analysis and exploitation techniques for the multipath data generated by this scaled RF environment. A portion of the material on multipath data analysis and exploitation is focused on developing techniques for identifying a optimum placement of receiver pairs for purposes of maximizing information content on a embedded target. In other words, data from the eight distributed receiver locations are analyzed and techniques are presented that allow for the selection of receiver pairs that provide the most information on targets that are embedded within the multipath environment. The last section of the paper discusses visualization and pseudo-imaging techniques for targets embedded in multipath environments.
We propose a novel approach to focus and geolocate moving targets in synthetic aperture radar imagery. The initial step is to detect the position of the target using an automatic target detection algorithm. The next step is to estimate the target cross-range velocity using sequential sub-apertures; this is done by forming low resolution images and estimating position as a function of sub-aperture, thus yielding an estimate of the cross-range velocity. This cross-range estimate is then used to bound the search range for a bank of focusing filters. Determining the proper velocity that yields the best focused target defines an equation for the target velocity, however both components of the targets velocity can not be determined from a single equation. Therefore, a second image with a slightly different heading is needed to yield a second focusing velocity, and then having a system of two equations and two unknowns a solution can be obtained. Once the target velocity is known the proper position can be determined from the range velocity. Synthetic data will be used with a point source target and both background clutter and noise added. The results support the development of staring radar applications with much larger synthetic aperture integration times in comparison to existing SAR modes. The basic idea of this approach is to trade-off the development of expensive phased-array technology for GMTI applications with the potential development of advanced processing methods that show potential for processing data over very large aperture integration intervals, to obtain similar GMTI geolocation results that would be compatible with current radar technology.
A set of approximate theoretical equations for the Doppler response of monostatic radar signals due to slowly
pivoting objects are derived. The treatment is based on physical models extracted from the mechanical engineering
community. Potential applications include analysis of load-based vehicle classification and detection of biological
movements such as human joint rotations. Several example calculations are presented based on the resulting
theoretical formulas. These examples include Doppler calculations for notional first-order vehicle suspension
models and first-order human joint (arm/leg) rotation models. Each set of example calculations includes two sets of
notional radar parameters in order to provide insight into potential Doppler pivot detection capabilities as a function
of basic radar parameters such as frequency and PRF (pulse repetition frequency).
Distributed airborne sensor geometries are considered that are comprised of multiple radar/comm transmit and receive nodes. Under this distributed robotic sensor concept, each of these radar transmit/receive nodes position-adaptively converge to the vicinity of a signal leakage point. A number of signal leakage point geometries are investigated that conform to geometries for typical building-type structures. The results include a set of electromagnetic computations that simulate the signal interaction and signal propagation between multiple leakage points. These signals are simulated via the modeling of materials that enclose "building-type" structures with a series of connected dielectric materials. For example, windows, walls, and doors are each modeled separately by a combination of suitable material properties. Signals from objects that are embedded within these "building-type" structures are also simulated via the development and application of appropriate geometrical and materials models. Analysis of the resulting simulated "leakage signals", that penetrate the surfaces of these "building-type" structures and are scattered from embedded objects within the indoor environment back to the simulated sensor-nodes in the outdoor environment, are presented. Interpretations of these results are included from a signal analysis perspective. These results also include approximate preliminary systems-type calculations with regard to this distributed position-adaptive UAV radar system concept. Potential applications are outdoor-to-indoor detection of objects-of-interest that are within a building via implementation of a intelligent multi-static sensor network.
Our proposed research is to focus and geolocate moving targets in synthetic aperture radar imagery. The first step is to estimate the target cross-range velocity using sequential sub-apertures; this is done by forming low resolution images and estimating position as a function of sub-aperture, thus yielding an estimate of the cross-range velocity. This cross-range estimate is then used to bound the search range for a bank of focusing filters. Determining the proper velocity that yields the best focused target defines an equation for the target velocity, however both components of the targets velocity can not be determined from a single equation. Therefore, a second image with a slightly different heading is needed to yield a second focusing velocity, and then having a system of two equations and two unknowns a solution can be obtained. Once the target velocity is known the proper position can be determined from the range velocity.
The results of numerical electromagnetic simulation and analysis of a set of positive-adaptive UAV radar signals are presented. These signals are simulated via the modeling of materials that enclose “building-type” structures with a series of connected dielectric materials. For example, windows, walls, and doors are each modeled separately by a combination of suitable material properties. Signals from objects that are embedded within these “building-type” structures are also simulated via the development and application of appropriate geometrical and materials models. Analysis of the resulting simulated “leakage signals” that penetrate the surfaces of these “building-type” structures and are backscattered from embedded objects within the indoor environment back to the simulated outdoor environment are presented. The results of a signal analysis are presented in two categories. The first set of results illustrates signal trends that can be exploited by “position-adaptive” mini-UAV's to isolate effective “leakage points” in “building-type” structures. The second set of results illustrate signal trends from embedded objects after a particular “position-adaptive” mini-UAV has converged to a “leakage point.”
This paper extends simulation and target detection results from an investigation entitled "Self-Training Algorithms for Ultra-wideband SAR Target Detection" that was conducted last year and presented at the 2003 SPIE Aerosense Conference on "Algorithms for Synthetic Aperture Radar Imagery." Under this approach, simulated SAR impulse clutter data was generated by modulating a tophat model for the SAR video phase history with K-distributed data models. Targets were synthesized and "instanced" within the SAR image via the application of a dihedral model to represent broadside targets. For this paper, these models are extended and generalized by developing a set of models that approximate major scattering mechanisms due to terrain relief and approximate major scattering mechanisms due to scattering from off-angle targets. Off-angle targets are difficult to detect at typical ultra-wideband radar frequencies and are denoted as "diffuse scatterers." Potential approaches for detecting synthetic off-angle targets that demonstrate this type of "diffuse scattering" are developed and described in the algorithms and results section of the paper. A preliminary set of analysis outputs are presented with synthetic data from the resulting simulation testbed.
A position-adaptive radar system concept is presented for purposes of interrogating difficult and obscured targets via the application of low-altitude smart or robotic-type UAV platforms. Under this concept, a high-altitude radiating platform is denoted as a HUAV and a low-altitude “position-adaptive” platform is denoted as a LUAV. The system concept is described by two modes. In Mode-1, real-time onboard LUAV computation of a phase parameter denoted as “signal differential path length” allows the LUAV to position-adaptively isolate a “signal leakage point”, for example, between two buildings. After the LUAV position-adaptively converges to an optimum location, the system enters Mode-2. Under this Mode-2 concept, a technique denoted as “exploitation of leakage signals via path trajectory diversity” (E-LS-PTD) is developed. This technique is based on modulating scattering centers on embedded objects by implementing a fast trajectory on the HUAV while the LUAV is hovering in front of an “obscuration channel.” Analytical results include sample outputs from an initial set numerical electromagnetic simulations.
A COTS-based design for a monostatic position-adaptive radar concept is presented. The development and design effort is focused on a test experiment where a onboard radar-based instrumentation system allows a mini-UAV helicopter to hover back and forth in front of two large (side-by-side) “building-type” structures. Under this concept, the “smart” or “robotic” mini-UAV helicopter “position-adaptively” converges to a location between the two “building-type” structures in order to interrogate an object-of-interest that may be located between these “building-type” structures. Design issues with regard to major sub-systems and interfaces between these sub-systems are discussed. Applications for this type of system include intelligence gathering from indoor and outdoor urban environments and underground facilities via deployment a tier of position-adaptive mini-UAV’s.
An ultra-wideband (UWB) synthetic aperture radar (SAR) simulation technique that employs physical and statistical models is developed and presented. This joint physics/statistics based technique generates images that have many of the "blob-like" and "spiky" clutter characteristics of UWB radar data in forested regions while avoiding the intensive computations required for the implementation of low-frequency numerical electromagnetic simulation techniques.
Approaches towards developing "self-training" algorithms for UWB radar target detection are investigated using the results of this simulation process. These adaptive approaches employ some form of modified singular value decomposition (SVD) algorithm where small blocks of data in the neighborhood of a sliding test window are processed in real-time in an effort to estimate localized clutter characteristics. These real-time local clutter models are then used to cancel clutter in the sliding test window. Comparative results from three SVD-based approaches to adaptive and "self-trained" target detection algorithms are reported. These approaches are denoted as "Energy-Normalized SVD", "Condition-Statistic SVD", and "Terrain-Filtered SVD". The results indicate that the "Terrain-Filtered SVD" approach, where a pre-filter is applied in an effort to eliminate severe clutter discretes that adversely effect performance, appears promising for the purposes of developing "self-training" algorithms for applications that may require localized "on-the-fly" training due to a lack of accurate off-line training data.
A number of aspects of ultra-wideband radar target detection analysis and algorithm development are addressed. The first portion of the paper describes a bi-modal technique for modeling ultra-wideband radar clutter. This technique was developed based on an analysis of ultra-wideband radar phenomenology. Synthetic image samples that were generated by this modeling process are presented. This sample set is characterized by a number of physical parameters. The second portion of this paper describes an approach to developing a class of filters, known as rank-order filters, for ultra-wideband radar target detection applications. The development of a new rank-order filter denoted as a discontinuity filter is presented. Comparative target detection results are presented as a function of data model parameters. The comparative results include discontinuity filter performance versus the performance of median filtering and CFAR filtering.
A number of spectral feature computations for purposes of discriminating military targets from clutter are currently under investigation within the Radar Branch at the Air Force Research Laboratory. Results from a comparative performance analysis of these features are reported. The development and analysis of spectral phase computations are of particular interest since, for some 'hard clutter' environments, the use of amplitude-based discriminants does not generate a sufficiently low false alarm rate. These phase computations are based on the analysis of the Fourier phase function, analysis of the phase spectral density, and analysis of the bispectrum. Additional spectral features, such as features based on angular diversity, are also included within the scope of this investigation. The data for this investigation is comprised of some SAR images and image chips that were collected and generated under the DARPA/Air Force Moving and Stationary Target Acquisition and Recognition (MSTAR) Program.
In a proposed process diagnostic system, a template correlation routine is used to identify the pattern produced on a wafer by bad die. The same pattern recognition routine can be applied to the pattern produced on the wafer from the result or different types of failure of the die. The overall wafer pattern, the individual test patterns, and electrical measurements made during final test form the input data to an expert system. In turn, the expert system uses heuristic algorithms that identify probable process problems, The template correlation routines have been successfully developed and implemented. Current efforts are dedicated to the knowledge engineering phase of the project. The expert system will, at first, identify defects to the process level. Future refinements will permit the diagnostic tool to identify the defect by name as well as process. Successful development and implementation of this system will save the labor of manual investigation of anomalous events in the fabrication of VLSI devices. Used in conjunction with statistical process control, this system should improve VLSI device yield.