Final Long
Program
Monday - Tuesday - Wednesday - Thursday
Wednesday July
12
Session WeA1: Plenary
talk
Data Fusion in the Transferable Belief
Model
Prof. Philippe Smets, IRIDIA - Université Libre de
Bruxelles, Belgium
Abstract: When Shafer introduces his theory of evidence
based on the use of belief functions, he proposed a rule to combine the belief
functions induced by distinct pieces of evidence. Since then, theoretical
justifications of this so-called Dempster's rule of combination have been
produced and the meaning of distinctness has been assessed. We will presents
practical applications where the fusion of uncertain data is well achieved by
Dempster's rule of combination. It is essential that the meaning of the belief
functions used to represent uncertainty be well fixed, as the adequacy of the
rule depends strongly on a correct understanding of the context in which they
are applied. Missing to distinguish between the upper and lower probabilities
theory and the transferable belief model can lead to serious confusions, as
Dempster's rule of combination is central in the transferable belief model
whereas it hardly fits with the upper and lower probabilities theory.
In
order to illustrate the possibilities that are offered by the transferable
belief model when it comes to uncertain data fusion, we present some practical
applications. For each of them, the transferable belief model seems well
adapted whereas the classical probability theory might encountered problems,
usually because of some missing information that probability theory requires
and that is not available or worse, not existent.
Session WeB1: Resource
Management - 1
Chair: Jean-Pierre Le Cadre, IRISA/CNRS, Rennes, France
Co-Chair : Frédéric Dambreville, IRISA/CNRS, Rennes, France
1 - A Fuzzy Logic Resource Manager and
Underlying Data Mining Techniques
James F. Smith III, Robert D. Rhyne II, Naval Research Lab.,
Washington, DC, USA
Abstract: A fuzzy logic based expert system has been
developed that automatically allocates electronic attack (EA) resources in
real-time over many dissimilar platforms. The platforms can be very general,
e.g., ships, planes, robots, land based facilities, etc. Potential foes the
platforms deal with can also be general. This paper describes data mining
activities related to development of the resource manager with a focus on
genetic algorithm based optimization. The use of a database of scenarios
prevents the algorithm from having too narrow a range of behaviors, i.e., it
creates a more robust solution. The approach to optimization is a type of
co-evolution, i.e., both friend and foe agents simultaneously adapt within a
complicated environment perceived through various sensors such as: radar,
electronic support measures, etc. New rule classes and the five components of
the resource manager are discussed. Finally, the resource manager’s
multi-platform response is examined for multiple scenarios.
2 - Optimal Passive Receiver Location
for Angle Tracking
Kaouthar Benameur, Surface Radar Section, Defence Research
Establishment, Ottawa, Ontario, Canada
Abstract: This paper presents a class of optimization
problems dealing with selecting at each instant of time a strategy of
measurements for a passive receiver. The basic problem is then to compute an
optimal policy, during a specified observation time interval so that a
prediction accuracy is optimized. This problem of mesurement strategy
computation can be transformed into a deterministic control problem. It is
shown that the optimal measurement policy can be precomputed before the
measurements actually occur. A detailed description of this measurement
strategy is presented in the case of a receding horizon observation
interval.
3 - Sensor Management - Control and
Cue
Gee Wah Ng, Khin Hua Ng, L. T. Wong, DSO National Laboratories,
Singapore
Abstract: This paper presents the role of sensor
management with respect to sensor system and the fusion process. The important
roles and functions of sensor management are discussed. Multi-level
classification of the sensor management is presented. The control and cueing
aspects of sensor management are explored. An experiment is set to demonstrate
sensor manager controlling electro-optical (EO) sensor's moving direction and
sensor cueing process to track a target. A fuzzy controller is used in this
experiment. The fusion system used an interactive multiple model (IMM)
algorithm to give the estimated target direction..
4 - Scheduling Active and Passive
Measurements
Jean-Pierre Le Cadre, IRISA/CNRS, Rennes, France
Abstract: Many tracking systems involve basically active
and pssive subsystems. If it can be reasonably assumed that passive
measurements have no "cost", this is not true for active measurements. So, a
general problem is to scheduling active measurements, so as to combine them
optimally with the passive ones. More generally, we are interested by
optimizing controls in the estimation procedure.
Session WeB2: Tracking
Course - 1
Yaakov Bar-Shalom, University of Connecticut, Storrs, CT, USA
Short Course: Multitarget
Tracking and Multisensor Fusion
Session WeB3: Image
Fusion - 2
Chair: Amy L. Magnus, Intelligent Information AFRL/IFTD, NY,
USA
Co-Chair: Brian O'Hern, Air Force Research Laboratory, Rome, NY,
USA
1 - Fast Stereo Matching Using
Multilevel Enhancement
Jaeweon Kim, Shishir Shah, Wayne State University, Detroit, MI,
USA
Abstract: Stereo matching is a key problem in the area of
computer vision and photogrammetry. Stereo is a useful method for machine
perception and recovering 3D shape. Given a point in the left image, the
problem is to find its corresponding point in the right image, which typically
has additional image variations. Most existing techniques use a fixed window
for the purpose of calculation of the correlation coefficient values. The fixed
window approach leads to mismatch in areas lacking features and those without
much intensity variations. This paper presents a new approach of correct and
fast calculation for stereo matching. Some of the efficient and robust
implementation aspects of the stereo matching algorithms using thresholding
correlation values of previous pyramid structure that reduce the search area
for finding the best matching point is addressed. The disparity for each scan
line is chosen by selecting the position that gives the maximum correlation
coefficient from the pyramid structure. However, sometimes this maximum value
may be overridden by a spurious maximum. Ambiguities mainly come from image
noise, lack of intensity variation or geometric distortion. For disambiguation,
the adaptive window theory is applied to avoid mismatch in featureless area.
Adjusting threshold level at each level of the pyramid structure reduces
computing time and increases the accuracy for the next pyramid level. Results
obtained on both synthetic and real image sets are presented.
2 - A Multi-Level Bayesian Network
Approach to Image Sensor Fusion
Amit Singhal, University of Rochester, NY, USA
Jiebo Luo,
Christopher Brown, Eastman Kodak Company, Rochester, NY, USA
Abstract: Automatic main subject detection refers to the
problem of determining salient or interesting regions in an image. We propose
the use of a Bayesian network based approach to solving this problem in the
unconstrained domain of consumer photographic images. Various image sensors,
derived from classical computer vision literature, as well as other sources,
can provide evidences about main subject regions in images. A traditional
sensor fusion scheme, such as a Kalman filter, fuzzy logic, or simple Bayesian
estimation, does not provide sufficient expressive power to capture the
uncertainties and dependencies exhibited by such a system. We present a
multi-level Bayesian network that accurately models the system and allows for
sensor integration in an evidential framework. The multi-level Bayesian network
performs better than a simple single-level Bayesian network at accurately
combining various image sensor data to construct a belief map identifying main
subject regions in the image. A subsequent study also shows that the
multi-level Bayesian network performs better than a linear classification
scheme as well as one based on neural networks.
3 - 3D Structure and Motion Recovery by
Fusing Range and Intensity Image Sequences
Christophe Boucher, Jean-Charles Noyer, Mohammed Benjelloun,
Université du Littoral Côte d’Opale, Calais, France
Abstract: We propose in this article a 3D dynamic
reconstruction method based on the fusion of data from sequences of range and
intensity images. The vision system acquires both images of the scene at every
time. We use a line segment description of the objects of the scene, and more
precisely a point representation of the segment described by its extremities.
The detailed solution to this estimation problem lies on a global filter that
matches the segments through the range/intensity image sequences and fuses both
data to recover the 3D structure and motion of an object. The method performs
well on synthetic image sequences from two sensors and is now applied to an
experimental sequence of an object evolving according to a rotation/translation
motion. The scene is viewed by a range camera which delivers a range image and
an intensity (reflectance) image.
Session WeB4: Enabling
Computation
Chair: H. Martínez Barberá, University of
Murcia, Spain
Co-Chair: Roger Reynaud, IEF - University Paris XI, Orsay,
France
1 - Ontology Based Design of
Surveillance Systems with NUT
Vahur Kotkas, Jaan Penjam, Institute of Cybernetics at Tallinn
Technical University, Estonia
Enn Tyugu, Royal Institute of Technology
(KTH), Kista, Sweden
Abstract: This paper presents ontology-based programming
using the NUT language as a notation for semantics of domain knowledge. The
specification method and problem solving techniques are demonstrated on an
example of modeling and management of a radar surveillance system in order to
find optimal disposition and configuration of equipment. Structural synthesis
of programs - a technique essential for the domain knowledge handling is
briefly discussed.
2 - A Block-adaptive Blind Separation
Algorithm for Post-nonlinear Mixture of Sub- and Super-Gaussian
Signals
Yang Chen, Zhenya He, Southeast University, Nanjing, China
Abstract: The problem of blind separation of signal in
post-nonlinear mixture is addressed in this paper. The post-nonlinear mixture
is formed by a component wise nonlinear distortion after the linear mixture.
hence a nonlinear adjusting part placed in front of the linear separation
structure is needed to compensate for the didtortion in separating such
sugnals. The learning rules for the post-nonlinear separation structure are
derived by a maximum likelihood approach. An algorithm for blind separation of
post-nonlinearly mixed sub- and super-Gaussian signals is proposed based on
some previous works. Multilayer perceptrons are used in this algorithm to model
the nonlinear part of the separation structure. The algorithm switches between
sub- and super-Gaussian probability models during learning according to a
stability condition and operates in a block-adaptive manner. The effectivness
of the algorithm is verified by experiments on artificial and natural signals.
3 - Neural Networks for Sonar and
Infrared Sensors Fusion
H. Martínez Barberá, A. Gómez Skarmeta, M.
Zamora Izquierdo, J. Botía Blaya, University of Murcia, Spain
Abstract: The main goal of our work is to have a robot
navigating in unknown and not specially structured environments, and performing
delivery like tasks. This robot has both unreliable sonar and infrared sensors.
To cope with unreliability a sensor fusion method is needed. The main problem
when applying classical fusion methods is that there is no a priori model of
the environment, just because the robot first carries on a map building
process. There exist some simple methods for sensor fusion but, as we show,
they do not address all the specific issues of our desired robot task. This
way, we use neural networks for such fusion, and so we obtain more reliable
data. We discuss some important points related to the training procedure of
neural networks and the results we obtained.
4 - Expanded Researching on Knowledge
Discovery System
Bing-ru Yang, Dezheng Zhang , Jing Tang, Ying Zhou, University
of Science and Technology, Beijing, China
Abstract: The development of database technology,
every-increasing needs of enterprise management for the decision-making support
and the comprehensive application of AI are driving the theories and technology
of data mining and knowledge discovery growing rapidly. In recent years, data
mining and knowledge discovery has been paying more attention to and all kinds
of algorithm and tools are blooming. Their characteristics are overlapping of
subjects, fusion of several techniques, generalizing of data mining and
integrating of knowledge discovery. This paper discussed the development trend
of knowledge discovery based on the summary and analysis of the theory of
knowledge discovery in database and the actuality of technological method, and
introduced the mechanism of total data mining process, researching in
expansible structure and algorithm of KDD, and serially developing of software,
according to the theory of double base cooperation proposed by author
Session WeB5: Guidance
and Navigation - 1
Chair: Christian Musso, ONERA, Châtillon, France
Co-Chair: Subhash Challa, University of Melbourne, Australia
1- Comparative Analysis of Alternative
Ground Target Tracking Techniques
Chih-Chung Ke, State University of New York at Buffalo, NY, USA
Jesús Garcia Herrero, GPSS-SSR-ETSIT, Universidad
Politécnica de Madrid, Spain
James Llinas, State University of New
York at Buffalo, NY, USA
Abstract: There have been a lot of studies addressing
target-tracking problems, in which targets like aircraft and missiles can move
freely in the air without hard spatial constraints. Tracking ground targets is
much more difficult. Variable terrain structures not only limit the target
kinematics, but also degrade the quality of measurement data. We are
particularly interested in two among several categories of trackers for point
ground targets that have no spatial extent. The first is Kalman-based approach
in which a number of studies taking advantage of terrain information, such as
elevation and road, have been proposed. The second is based on the theory of
hidden Markov model (HMM) in which targets are assumed to move among discrete
spatial cells at discrete time instants. It has been known that the terrain
information may be incorporated to retrieve the actual history of target
locations. This paper focuses on tracking a single ground target via both types
of approaches. Various Kalman techniques were implemented. When the ground
information, a road network in our study, is unavailable, the conventional
Kalman filter and an interacting multiple-model (IMM) estimator with two models
had roughly the same performance in terms of estimation errors. To reduce the
errors due to transversal maneuvers, we developed three methods to take road
structures into account. The first was to tune the variance of the process
noise, depending on if the on-road target was moving straight or was ready for
making a turn or maneuver. The second method, named curvilinear model, was a
better choice that considered the target trajectory as a circular arc during
the transition between road segments. The stability problem might occur if the
jump of road orientation is too high. The third, which outperformed the other
two for transversal errors, involved an additional stage to preprocess the
sensor measurements. Besides, an adaptive HMM tracker was also developed under
the same scenario. To save computation time, a partial scenario, which covered
the entire road path of interest, was partitioned into several subscenarios.
Each of them was associated with an HMM where the Viterbi algorithm was
performed with road-based transition array. Joining all subtracks from each HMM
formed the complete track. It turned out that the adaptive HMM tracker produced
small transversal errors but large longitudinal errors, compared to
Kalman-based techniques. Generally speaking, Kalman-based trackers are known to
be efficient and robust, whose performance can be improved by the proposed
methods. The adaptive HMM-based tracker can freely incorporate any terrain
features, target kinematics, and even military doctrines into the transition
array such that the track can be more accurate.
2 - Road Detection and Vehicles
Tracking by Vision for an On-Board ACC System in the VELAC
Vehicle
R. Chapuis, F. Marmoiton, R. Aufrère, F. Collange, J-P.
Dérutin, LASMEA/CNRS, Université Blaise Pascal, Aubiére,
France
Abstract: This paper presents a method designed to detect
and track vehicles on highway in a safety improvement purpose. The goal of this
kind of system is to regulate the speed of a vehicle so as to respect safety
distances relative to vehicles ahead. The method is exclusively based on
monocular computer vision and uses two algorithms. The first one is able to
locate the lane borders in the image, and to deduce the 3D shape of the road
axis. The second algorithm detects, tracks and compute the 3D location of
vehicles ahead by using fixed lights embedded on these ones. By combining the
results of the two algorithms, a fusion step permits to know were are the most
dangerous vehicle according to its position, speed and circulation lane. The
method has been implemented onour experimental vehicle VELAC and the whole
system operates in real-time conditions.
3 - Temporal Sequence Recognition Using
Uncertain Sensor Data
Michèle Rombaut, CREATIS, Lyon, France
S.
Loriette-Rougegrez , J.M. Nigro, LM2S UTT, Troyes, France
I. Jarkass,
Université Libanaise, Institut Universitaire de Technologie, Saida,
Liban
Abstract: The problem addressed in this paper concerns
the temporal sequence recognition for a dynamic system. Several formal models
can be used such as rule based systems, or graphs such as transition graphs or
Petri nets in order to describe the sequences to be recognized. Then, according
to the inputs got from the system's sensors at different times, the goal is to
evaluate the confidence into the fact that the sequence isinprogress. In this
paper, the confidence is modeled by a distribution of mass of evidence proposed
in the Dempster-Shafer's theory.
4 - Recent Particle Filter Applied to
Terrain Navigation
Christian Musso, Nadia Oudjane, ONERA, Châtillon, France
Abstract: A Recent particle method, the Local Regularized
Rejection Particle Filter L2RPF is applied here in terrain navigation. An
aircraft measures periodically the relative elevation. By means of a digital
elevation map, the goal is to estimate the absolute position and velocity of
the aircraft. Moreover, an inertial navigation system (INS) drives the
generation of the particles (fusion of L2RPF and the INS). The conditional
density of the state is recursively estimated. The proposed filter allows a
precise correction step in a given computational time. For this problem, the
Kalman filter is inadapted (multimodality) and batch methods are expensive
(grid methods in a 6-dimentional state space).
Session WeC1: Resource
Management - 2
Chair: Michel Prenat, Thomson-CSF Optronique, Guyancourt,
France
Co-Chair : Véronique Cherfaoui, Heudiasyc CNRS,
Compiègne, France
1- Selected Problems of MFR Resources
Management
W. Komorniczak, J. Pietrasinski, Military University of
Technology, Warsaw, Poland
Abstract: An idea of a Multi Function Radar resources
management is described. In this kind of radar, in contrast to a classic
solutions, a radar controller has to manage a lot of parameters and
particularly, resources. Problems of radar resources management and its
optimization are described in the paper. The multiplicity of limitations
connected with resources to use, makes resources management process very
complicated, but necessary. The resources to be controlled as well as
optimization variables, parameters and goal function are characterized. The
model of a system of radar resources controller simulation is presented. The
priority assignment process for detected objects is discussed too. The methods
of learning of priority assignment module are characterized.
2 - Detection with Spatial and Temporal
Optimization of Search Efforts Involving Multiple Modes and Multiple Resources
Management
Frédéric Dambreville, Jean-Pierre Le Cadre,
IRISA/CNRS, Rennes, France
Abstract: This paper deals with optimization of splitable
resources aimed to the detection of a moving target following a Markovian
movement or a conditionally deterministic motion. Our work extends Brown's
spatial optimization method. By use of a generalized linear formalism, we
developed a method for optimizing both spatially and temporally (modeling
resource renew), with management of multiple resource types or multi-modes
resources. Such optimization involves also the fusion of several detection
tools, in order to make them work together efficiently.
3 - Analysis of the Multisensor
Multitarget Tracking Resource Allocation Problem
Pierre Dodin, Julien Verliac, Vincent Nimier, ONERA,
Châtillon, France
Abstract: This paper deals with a study of the
multisensor management problem. The main tool is the classical assignment
formulation, using Kullback-Leibler entropy as costs. In order to use the
benefit brought by the data fusion, coalitions or pseudo-sensors must be
created at each step of time, creating an exponential calculus of all the
possible sensor partitions. We compare this method and a predefinite strategy
using different scenarios.
4 - Study of the Temporal Allocation of
Two Passive Infrared Sensors in a Multitarget Environment
Marie De Vilmorin, Philippe Vanheeghe, Ecole Centrale de Lille,
Villeneuve d'Ascq, France
Michel Prenat, Thomson-CSF Optronique,
Guyancourt, France
E. Duflos, Ecole Centrale de Lille, Villeneuve d'Ascq,
France
Abstract: The topic of this paper is the optimization of
the management of the lines of sight of two infrared sensors in a multitarget
environment. Some general trends for the elaboration of optimal strategies have
been undercored by the study of a strategy based on the fusion of these passive
sensors. The main result which has been used is the completion principle, taht
is, the equivalence in Bearing Only Tracking (BOT) between the asymptotic
behavior of the real maximum likelihood estimator and the one obtained by
supposing a linear measurement model.
Session WeC2: Tracking
Course - 2
Yaakov Bar-Shalom, University of Connecticut, Storrs, CT, USA
Short Course: Multitarget
Tracking and Multisensor Fusion
Session WeC3: Image
Fusion - 3
Chair: Belur V. Dasarathy, Dynetics Inc., Huntsville, AL, USA
Co-Chair: Vladimir Petrovic, Manchester School of Engineering, UK
1 - A Real Time Pixel-Level Based Image
Fusion Via Adaptive Weight Averaging
Eric Lallier, Mohamad Farooq, Royal Military College of Canada,
Kingston, Ontario, Canada
Abstract: A novel pixel-level image fusion scheme for
thermal and visual images is presented in this paper. The image fusion
technique rests on physical characteristics of targets deemed of interest in a
surveillance scenario. Each picture element (pixel), in both the thermal and
visual images, is assigned a weight proportional the interest associated with
it. Interest is defined as “not natural” or “man-made”. A
weighted average of the intensity images representing the thermal and visual
modalities is then performed for every corresponding pair of visual and thermal
picture elements to obtain the fused image. For the thermal images, elements
that are warmer or cooler than their environment (background) are deemed to be
of “interest”. To this end, the thermal weights are associated with
the divergence of the intensity of these pixels from the image mean intensity.
For the visual images, the facts that the “targets of interest” are
usually larger then the instantaneous field of view (IFOV) of the visual sensor
and have a reflection behaviour that is more specular are used. The visual
weight determination is based on the local variance in space and time of the
intensity of the visual pixels. The performance of this technique is compared
to a number of existing techniques in the literature. The results reveal that
the proposed technique performs better than those in the literature. In
addition, it also reveal that the proposed technique is more robust than those
in the literature.
2 - On the Effects of Sensor Noise in
Pixel-Level Image Fusion Performance
Vladimir Petrovic, University of Manchester, UK
C. Xydeas,
University of Lancaster, UK
Abstract: This paper considers image fusion under the
condition that input image quality is reduced by sensor noise. The aim is
twofold: i) to develop appropriate metrics which measure the effect of input
sensor noise on the performance of a given pixel-level image fusion system and
ii) to employ these metrics in a comparative study of the robustness of typical
image fusion schemes whose input is corrupted by sensor noise.
3 - More the Merrier … or is it ?
- Sensor Suite Augmentation Benefits Assessment
Belur V. Dasarathy, Dynetics Inc., Huntsville, AL, USA
Abstract: It is often implicitly assumed in the
information fusion field that augmentation of a multi-sensor suite with
additional sensors defacto enhances overall system performance because of the
increase in the data being input to the fusion process. In this study, an
explicit assessment of the validity of this assumption is made in terms of
delineating the sensor characteristics domain wherein this is true (i.e., where
fusion benefits do indeed increase) and quantitatively determine the extent of
such benefits. Initially for illustrative purposes, a two-sensor suite
augmented by a third sensor is used as a case study for this assessment. The
consensus fusion logic, which is symmetric relative to the multiple sensors in
the sensor suite, is employed as an example in this assessment process. The
scope for generalizing this assessment to higher dimensional multi-sensor
suites as well as other types of fusion logic is also discussed.
Session WeC4: Symbolic
and Numeric Information: Hybrid Approaches - 1 – Invited Session
Chair: Galina Rogova, CUBRC, Buffalo, NY, USA
1 - Peircean Semiotics: A New
Engineering Paradigm for Automatic and Adaptive Intelligent Systems
Design
E.T. Nozawa, Lockheed Martin Aeronautics Company, Marietta,
Georgia, USA
Abstract: The intent of this paper is to bring before the
Information Fusion community highlights of Peircean Semiotics as defined by
Charles Sanders Peirce. An attempt will be made to show that Peircean Semiotics
has potentially a very revolutionary role to play in the further development of
Information Fusion and in the advanced development of Artificial Intelligence,
Cognitive Science, Natural Intelligence Science, and Information Processing
Science and Technology in general. The transition to the Information Age and
the explosive growth in knowledge is occurring at a time when the traditional
humanistic philosophies and scientific philosophies have run their course and
are unable to provide adequate guidance for the development of Advanced
Automated Reasoning-Based Belief systems that are simultaneously Open-Systems
and Human-Centered Systems.
2 - Semeiotic Data Fusion
Robert W. Burch, Texas A&M University, College Station, TX,
USA
Abstract: Approaches to problems of data fusion typically
involve methods, such as statistical inference and Baysian analysis, that are
quantitative in nature. Quantitative methods, however, are often poorly suited
for dealing with types of data that are essentially qualitative and relational
in nature, for example, organizational structure, economic and political
relations, and psychological, sociological, and historical conditions. A
directly qualitative/relational approach to data analysis arose in Moscow
during the last decade of the Societ period, in the work of Professor Victor
Konstantinovich Finn. This approach currently thrives in Finn's Intellectual
Systems Laboratory in VINITI (The All-Russian Institute for Scientific and
Technical Information). By using a "semeiotic" approach--that is, one based on
mathematical logic and the formal analysis of concepts--Finn's so-called "JSM
Method" is a novel tool for data fusion that has proved itself effective in a
number of applications. These range from chemistry and pharmacology to
sociology and labor relations. The present paper presents basic ideas of Finn's
JSM Method of Automatic Generation of Hypotheses.
3 - HyM: a Methodology for the
Development of Integrated Hybrid Intelligent Information Systems
Simon Kendal, X. Chen, A. Masters, University of Sunderland,
UK
Abstract: HyM is a hybrid methodology for the development
of large-scale and complex integrated hybrid intelligent information systems,
which combines traditional information system development methods with
knowledge-based system development methods. The methodology is an integration
of four existing methods using two integration process approaches:
intra-process and inter-process. In the requirements analysis phase, a
structured method is applied to function analysis, an information modeling
method is applied to data analysis, and a knowledge acquisition method is
applied to knowledge analysis. An intra-process approach is then used to
integrate these techniques together using consistency rules. In the design
phase, the inter-process approach is used to transform the requirements
analysis to object-oriented design by a transformation method. Finally, the
object-oriented method is applied to the design and implementation of hybrid
information systems. This methodology takes advantage of the four individual
methods to overcome the limitations of each. It is applicable to the
development of traditional information systems, knowledge-based systems, and
large and complex co-operative and intelligent information systems.
Session WeC5: Guidance
and Navigation - 2
Chair: Jose R. Casar, Universidad Politécnica de
Madrid, Spain
Co-Chair: Itzhack Y. Bar-Itzhack, Technion Institute of
Technology, Haifa, Israel
1- Data Fusion Versus Passive Filtering
for Angular Velocity Estimation
Itzhack Y. Bar-Itzhack, Technion-Israel Institute of Technology,
Haifa, Israel
Abstract: This paper presents two approaches to
estimating the angular velocity of a spacecraft. One approach, which is
basically a data fusion approach, uses passive filtering, while the other uses
active filtering. According to the first approach differentiated spacecraft
attitude data is processed to yield noisy angular velocity information which is
then passed through a low pass filter. According to the second approach, an
active filter that yields the angular velocity, processes either the
differentiated attitude data, or just the attitude data. The active filter
blends the information rendered by the spacecraft dynamics with information on
either the differentiated attitude or on the attitude itself. Examples are
presented which use real spacecraft data.
2 - Multisensor Data Integration in the
NASA/Stanford Gravity Probe B Relativity Mission
M.I.Heifetz, G.M.Keiser, A.S.Krechetov, A.S.Silbergleit,
Stanford University, CA, USA
Abstract: Gravity Probe B (GP-B) is a gravitational
experiment designed to measure two predicted by General Theory of Relativity
precessions of a free-falling gyroscope placed in a polar orbit about the
Earth. The frame-dragging effect (drift perpendicular to the orbital plane) has
never been directly measured before, while the geodetic effect (drift in the
orbital plane) will be measured with an unprecedented accuracy. GP-B Data
Analysis is an example of the multi-sensor information fusion: it requires the
integrated processing of the data from nine physical sources of information:
four science gyroscopes, the science telescope, the attitude control system of
the spacecraft, on-board GPS receivers, NASA/JPL Earth ephemerides, and the
astronomical data on the proper motion of the reference star. This paper
presents the core filtering approach to the state-estimation of the GP-B
system. Two-step nonlinear filter (that may be applied for the wide class of
nonlinear measurements) is discussed and the specifics of its implementation
for the GP-B data analysis is presented.
3 - Fault Detection and Isolation using
Interval Analysis: Application to Vehicle Monitoring
Pascal Bouron, Dominique Meizel, Université de
Technologie de Compiègne, France
Abstract: This paper gives an example of using set
membership techniques for detecting component fault and model failures and
isolating the cause of the fault. Set membership estimation techniques can
inherently detect model failure when the estimated set becomes empty. This
property is here applied for fusing parity equations generated by an analytic
redundancy study. For each parity equation, one defines a symbolic indicator
that individually characterizes a certain or possible failure. Defining a
{cause/effect} array makes it possible to isolate the certain or possible
causes of the defect. The method is developped within a pedagogical example of
the kinematic model of a vehicle.
4 - ADS Bias Cancellation Based on Data
Fusion with Radar Measurements
Juan A. Besada, Jesus Garcia, Gonzalo de Miguel, Jose R. Casar,
Universidad Politécnica de Madrid, Spain
Gonzalo Gavin, INDRA-DTD,
Madrid, Spain
Abstract: This paper describes a complete tracking
function for air traffic control based on the fusion of radar and ADS-B
messages. For this system, the most important terms to be corrected are low
frequency errors from both radar sensors and ADS measurements, because the
other components can be easily lowered through filtering. In the paper we
propose innovative methods both for radar registration, and for low frequency
ADS-B errors removal. Those two processes form the core of the tracking
function. Additionally, very accurate measurement conversions are included, to
avoid corrupting the estimations. The results clearly show the ability of the
system to improve ADS-B based tracking and to obtain accurate estimations of
radar biases. This second aspect is very interesting for improving tracking non
ADS-B equipped aircraft.
Session WeD1: Target
Tracking - 6 – Track Fusion - 3
Chair: Thiaglingam Kirubarajan, University of Connecticut,
Storrs, CT, USA
Co-Chair: Peter K. Willett, University of Connecticut,
Storrs, CT, USA
1- Multisensor Multitarget Tracking
with Central-to-Local Feedback
Carl G. Looney, Yaakov L. Varol, Sheng Tang, Computer Science
Department, University of Nevada, Reno, USA
Abstract: This paper investigates the effects on central
tracks of the feedback of central track data to local sensing and tracking
stations. The sensors are local radar stations that also initiate and update
local tracks from noisy measurements of range, azimuth and elevation using
abg-filters. They send their current track data to the central tracking
station, which fuses its own track predictions with the local data to update
its central tracks via abg-filters. We study the two cases of with and without
missing data. Our simulations use low, medium and high levels of noise power.
They show that the feedback reduces the error between the central tracks and
the actual trajectories by an average of about 50%.
2 - Effects of Cross-Covariance and
Resolution on Track Association
B. La Scala, The Preston Group, Richmond, Australia
A.
Farina, ALENIA Marconi Systems, Italy
Abstract: This paper considers the effects on track
association of two features that are often neglected in analysis. These two
features are the cross-covariance between the two tracks of the same target;
and the effects of a loss of resolution in a sensor. The sensors modelled in
the paper are non-homogeneous in both state and measurement space and a method
for calculating the cross-covariance for such dissimilar sensor is derived.
Also, a simple resolution model is proposed. The paper uses Monte Carlo
simu-lations to illustrate the effects of these two features on a number of
track association methods.
3 – MILORD, an Application of
Multifeature Fusion for Radar NCTR
V. Nimier, A. Bastière, ONERA, Châtillon, France
N. Colin, M. Moruzzis, Thomson-CSF/ Airsys, Bagneux, France
Abstract: Among the various topics addressed by data
fusion, the application to Target Recognition by Radar is one of major interest
because it is expected that good performance will come out from a process in
which several complementary information will be merged. In the framework of the
PEA ("Programme d'Etudes Amont") MILORD ("Moyen d'Identification Lointaine d'
Objectifs Radar Désignés"), studies are currently conducted for
defining the best techniques to be used for this function. This paper
summarises the current results which were obtained so far in this domain. After
having presented, in Chapter 2, the objectives and interests of fusion within
the domain of Target Recognition by Radar, major fusion techniques are
discussed in Chapter 3. Application of fusion to MILORD and current results are
then summarised in Chapter 4, before conclusions which are drawn in Chapter
5.
4 - Multi-Mode Detection with Markov
Target Motion
Danna Sinno, D. Cochran, D.R. Morrell, Arizona State University,
Phoenix, AZ, USA
Abstract: This paper addresses the problem of
configuration of a detection system offering multiple modes of operation that
differ in their detection performance and geographical coverage. A technique
for optimal mode selection based upon minimizing Bayesian risk is formulated
and demonstrated for the case of a two-mode system with a moving target. The
dynamics of the target are described by a Markov model.
Session WeD2: Tracking
Course - 3
Yaakov Bar-Shalom, University of Connecticut, Storrs, CT, USA
Short Course: Multitarget
Tracking and Multisensor Fusion
Session WeD3: Remote
Sensing
Chair: Basel Solaiman, ENST-Bretagne, Brest, France
Co-Chair: Belur V. Dasarathy, Dynetics, USA
1 - Multimodality Image Registration
and Fusion Using Neural Network
Mostafa G. Mostafa, Aly A. Farag, Edward Essock, University of
Louisville, KY, USA
Abstract: Three-dimensional (3-D) digital models of
terrain region are essential for various remote sensing applications.
Multimodality image registration and fusion are essential steps in building 3-D
models from remote sensing data. In this paper, we present a neural network
technique for the registration and fusion of multimodality remote sensing data
for the reconstruction of 3-D models of terrain regions. A FeedForward neural
network is used to fuse the intensity data sets with the spatial data set after
learning its geometry. Results on real data are presented. Human performance
will be assessed on several perceptual tests in order to evaluate of the fusion
results.
2 - Radar Image Fusion by Multiscale
Kalman Filtering
Giovanni Simone, Francesco C. Morabito, University of Reggio
Calabria, Italy
Alfonso Farina, Alenia Marconi Systems, Roma, Italy
Abstract: In this paper, we propose the application of
the Multiscale Kalman Filter (MKF) to the fusion of images of the same scene,
acquired by different radars operating with different resolutions. The images
have been spatially registered and they have been processed by a MKF processor,
to compute an output image with improved information carried by each input
image. The full fledged Multiscale Kalman Filter is described, and a basic
model is tested to fuse AIRSAR and SIR-C images.
3 – Wide Area Fire Surveillance by
Infrared Digital Signal Processing
L. Vergara, P. Bernabeu, J.Igual, Universidad Politécnica
de Valencia, Spain
Abstract: A general scheme for automatic signal detection
in scanning surveillance systems is proposed. We consider the problem of
deciding if we have an alarm, based on the data measured in the cell under
analysis in different consecutive scanning time instants. The proposed scheme
includes a linear predictor and a subspace model for the signal to be detected,
in an effort to maximise the detector performance. Although it may have general
applicability in scanning surveillance problems, we focus the work on the
problem of wide-area uncontrolled fire detection by means of infrared radar.
4 - Segmentation of Airborne
Hyperspectral Images by Integrating Multi-Level Data Fusion
Marc Lennon, M.C. Mouchot, Grégoire Mercier, Basel
Solaiman, ENST-Bretagne, Brest, France
L. Hubert-Moy, Université de
Rennes, France
Abstract: This paper deals with the extraction of bocage
network from hyperspectral images acquired with the Compact Airborne
Spectrographic Imager (CASI). The strategy of segmentation integrates several
levels of data fusion allowing to take a decision concerning the membership of
each pixel to the bocage network from the large set of original data. The first
level leads to quantify the membership of each pixel to specific features of
the bocage. It includes data fusion based on physical properties, geometric
context-dependant fuzzy fusion with an original consistency measure and
geometric fusion of decisions. The second level is a fuzzy fusion of methods
allowing to quantify the membership of each pixel to the bocage network.
Finally, the third level consists in post-processing the data with a
context-dependent fusion of decisions to obtain the final map of the
bocage.
Session WeD4: Symbolic
and Numeric Information: Hybrid Approaches - 2 – Invited session
Chair: Galina Rogova, CUBRC, Buffalo, NY, USA
1 - Dual aspects of a Multi-Resolution
Grid-Based Terrain Data Model with Supplementary Irregular Data
Points
Fredrik Lantz, Erland Jungert, Swedish Defense Research
Establishment, Linköping, Sweden
Abstract: Digital terrain data models in high resolution
are required in applications for visualization but also, e.g. for
identification of various types of terrain features. These two aspects are in a
way contradictory since the former application require a large number of data
points to represent the high resolution, while the latter cannot deal with such
a large number of data points without high demands for heavy computational
powers. A solution to this problem is a structure that includes quantitative
characteristics for visualization and a qualitative representation for feature
analysis. A digital terrain data model characterized with these dual aspects
has been designed and will be presented in this work.
2 - An Agent based Combat Information
Processing System
Phillip Emmerman, Uma Movva, Larry Tokarcik, US Army Research
Laboratory, Adelphi, MD, USA
Carolin Gasarch, Timothy J. Rogers, V.S.
Subrahmanian, University of Maryland, College Park, MD, USA
Abstract: Tactical battlefield applications require that
agents monitoring battlefield events be able to dynamically react to events and
autonomously take actions that are in the best interest of the agent (and the
command and control system to which the agent belongs). In this paper, we
describe how IMPACT - Interactive Maryland Platform for Agents Collaborating
Together - has been used to agentize selected parts of a large scale
battlefield visualization demonstration system entitled the Combat Information
Processor (CIP). The CIP has been designed by the US Army Research Laboratory
to demonstrate and experiment with large scale, tactical battlefield
visualization concepts. We describe how this agentization improves the
functionality of CIP significantly.
3 - Automatic Air Target To Air Line
Association
Martin Oxenham, Defense Science and Technology Organisation,
Salisbury, Australia
Abstract: Australia's Strategic Policy states that
surveillance of the sea-air-gap to the north of Australia is a key element of
Australia's defence strategy. This surveillance is primarily afforded by a
suite of ground-based microwave and over-the-horizon (OTH) radars. The fusion
of the data and tracks from these sensors is an area of active research in
support of air picture compilation. To ensure the best results from the fusion
process, it is critical that the estimates of the target state from each
constituent sensor be as accurate as possible. In this paper, the theoretical
aspects of the problem of automatically associating air targets with airlanes
are investigated with the aim of providing a means of removing possible biases
in OTHR target state estimates caused by ionospheric effects. Furthermore, some
of the benefits of automatic air target to airlane association for situation
assessment are briefly examined.
Session WeD5: Guidance
and Navigation - 3
Chair: Patrick Maupin, LIVIA, Montréal, Québec,
Canada
Co-Chair: Stéphane Paradis, DRE Valcartier,
Val-Bélair, Québec, Canada
1 - Time Recovery through Fusion of
Inaccurate Network Timing Assistance with GPS Measurements
Jari Syrjärinne, Nokia Mobile Phones, Tampere, Finland
Abstract: In this paper fusion of inadequate timing
information from GPS space vehicles and from a cellular network is studied.
Neither of the sources is accurate enough to be used for exact timing alone,
but using the time recovery method proposed by the author, exact timing can be
eventually derived from the fusion of these sources. Naturally, the positioning
accuracy is also positively affected when accurate time is recovered. The
proposed method is based on minimization of a quality of fit value. The quality
of fit value used in this paper is sum of squared residuals obtained from a
Least Mean Squares based positioning procedure, widely used in point-solution
type GPS applications. The proposed method is tested and evaluated using both
simulated and real data.
2 - Fusion of Heterogeneous Sensors for
the Guidance of an Autonomous Vehicle
Jan C. Becker, Technical University, Braunschweig, Germany
Abstract: This paper describes the sensor fusion system
of an autonomous vehicle for automated vehicle testing. The vehicle sensor
system for object-detection consists of a stereo vision sensor, four
laserscanner (lidar) and a radar sensor. The sensor system is designed to
totally cover the vehicle environment with a high redundancy in front of the
vehicle. The sensor fusion system of the vehicle consists of a data alignment,
a data association and a state estimation module. An adaptive information
filter is used for the fusion of the associated targets from different sensors.
The fused targets are input to the path planning and guidance system of the
vehicle to generate a collision free motion of the vehicle.
3 - Sensor Data Fusion Using Kalman
Filter
Jurek Z. Sasiadek, P. Hartana, Carleton University, Ottawa,
Ontario, Canada
Abstract: Autonomous Robots and Vehicles need accurate
positioning and localization for their guidance, navigation and control. Often,
two or more different sensors are used to obtain reliable data useful for
control system. This paper presents the data fusion system for mobile robot
navigation. Odometry and sonar signals are fused using Extended Kalman Filter
(EKF) and Adaptive Fuzzy Logic System (AFLS). The signals used during
navigation cannot be always considered as white noise signals. On the other
hand, colored signals will cause the EKF to diverge. The AFLS was used to adapt
the gain and therefore prevent the Kalman filter divergence. The fused signal
is more accurate than any of the original signals considered separately. The
enhanced, more accurate signal is used to guide and navigate the robot.
4 - Multiple Correspondence Analysis
for Highly Heterogeneous Data Fusion. An Example in Urban Quality of Life
Assessment.
Patrick Maupin, LIVIA, Ecole de technologie supérieure,
Montréal, Québec, Canada
Philippe Apparicio,
Université du Maine, Le Mans, France and INRS-Urbanisation,
Montréal, Canada
R. Lepage, LIVIA, Ecole de technologie
supérieure, Montréal, Québec, Canada
Basel Solaiman,
ENST-Bretagne, Brest, France
Abstract: The aim of this paper is to present the
preliminary results obtained through a composite data processing system using
Multiple Correspondence Analysis in order to perform data fusion and deduce
rules of spatial organisation. After having described a case study on the
cartography of Ambrosia artemisiifolia (common ragweed), currently investigated
on the Urban Community of Montreal (Canada) territory, the authors will present
an overview of the methods implemented for the digitalisation and interpolation
of data and discuss on methodological problems raised in the curse of the
study. Finally, avenues for future research will conclude this paper.
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2000
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