Final Long
Program
Monday - Tuesday - Wednesday - Thursday
Monday July 10
Session MoA1: Plenary talk of
Prof. David Schum
Information Fusion and Inference
Networks: Evidential Foundations
Prof. David Schum, George Mason University, USA
Abstract: Devices frequently employed in the fusing of information in
many situations come in the form of complex inference networks. The
construction and analysis of inference networks have a surprisingly long
history, dating back to 1913 in the work of an American legal evidence scholar
named John H. Wigmore. Methods for probabilistic analyses of complex inference
networks have a more recent history and now form an area of vigorous research.
Many of the current strategies for analyzing inference networks rest on
extensions of Bayes's rule and are collectively referred to as Bayes's Nets.
Inference networks can take many forms and can capture an assortment of
probabilistic interactions or nonindependencies among the variables represented
on an inference network. Most of the current work on Bayes's Nets has involved
the development of algorithms for the efficient propagation of probabilities
throughout a network as new evidence arrives. But not so much attention has
been paid to the fact that there are many logically distinguishable and
recurrent forms and combinations of evidence that can serve to activate an
inference network. Different forms of evidence require different methods for
establishing the credibility or believability of evidence. This is a most
important step in the fusion of evidence since credibility-related
considerations form the very foundation for all subsequent arguments based on
evidence. Part of my talk involves how these important credibility-related
foundations are established in the analysis of inference networks. It is also
true that evidence performs different roles in the analysis of inference
networks. Some evidence directly instantiates nodes or probabilistic variables
on an inference network. Such evidence is said to be directly relevant
evidence. But other evidence, termed indirectly relevant or ancillary evidence,
serves to justify the probabilistic strength of the arcs or linkages on an
inference network. There is some controversy at present about the role of
ancillary evidence in the analysis of Bayes's Nets that I will also address. In
the process, I will show how many of the original insights Wigmore had 1913
about the construction of inference networks deserve more serious consideration
than they are in fact receiving today.
Session MoB1: Plenary Panel
Discussion
FUSION, Vision and
Challenges
Organized by Rabinder Madan, Office of Naval Research, Arlington, USA,
James Myers, Ballistic Missile Defense Organization, USA,
Ivan Kadar, Consultant, Northrop Grumman Corporation, USA
Participants: Dr. Rabinder N. Madan - USA, Dr. Gabriel Ruchet -
France, Lt. Col. Dr. James Myers - USA, Dr. Ben Wynne - UK, Dr. David Kleinman
- USA, Dr. Jean-Pierre Le Cadre - France, Dr. Sebastiano Serpico - Italy, Dr.
William D. Blair - USA, Dr. Anthony Hyder - USA, Dr. Ivan Kadar USA
Session MoC1: Target Detection
and Recognition - 1
Chair: Pramod K. Varshney, Syracuse University, NY, USA
Co-Chair: Amy L. Magnus, Intelligent Information AFRL/IFTD, NY, USA
1 - A Suboptimum Permutation Test for Radar
Detection in Log-Normal Clutter Environments
Francisco Alvarez-Vaquero, José L. Sanz-González, Universidad
Politécnica de Madrid, Spain
Abstract:In this paper we have used a coherent log-normal model for
the radar clutter: the in-phase and quadrature components of clutter have been
modeled to give a log-normal amplitude distribution and a Gaussian distribution
of the phase. We have compared this model with another one in which the
distribution of the phase is uniformly distributed. Also, we present
detectability curves of the permutation test under log-normal noise
environments and different types of target models (nonfluctuating, Swerling I
and Swerling II). We analyze the detector performance in terms of detection
probability (Pd) versus signal-to-noise ratio (SNR) for different parameter
values: the integrated pulse number N, the noise reference samples M, and the
false alarm probability (Pfa). Finally, we show computer simulation results for
correlated clutter, discussing the meaning of those simulations.
2 - Target Classification by Autoregressive
Modeling Using Range Extent Profiles
Mahendra K. Mallick, Stefano Coraluppi, Alphatech, Inc., Burlington, MA, USA
Abstract: We present a novel algorithm based on autoregressive (AR)
modeling for analyzing the separability and classification of ground target
models using range extent data. Given a range extent profile of a target, we
estimate the appropriate model order for the data using the Akaike information
criterion (AIC). This prevents under-fitting or over-fitting of the data.
Previous researchers have shown that even if the actual process is not an AR
process, the AR model serves as a reasonable model for a wide class of
practical problems. Error modeling for the range extent data is extremely
difficult due to the complex nature of the scattering process, uncertainties in
the channel, sensor state, target dynamics, and estimation of the range extent
from a range profile. Therefore, our data driven approach serves as a useful
algorithm for analyzing target model separability and classification. We apply
the algorithm to simulated range extent data and obtain good classification
results. We plan to test the algorithm further with real range extent data.
3 - On Parametric Detection of Small Targets in Sea
Clutter
H.Einar Wensink, Hollandse Signaalapparaten B.V., Hengelo, The Netherlands
Abstract: A new algorithm is presented that instantaneously estimates
the clutter characteristics in the environment of the radar cell that is
processed. No a priori information is used, since the algorithm operates
directly on the data of the incoming burst. It is a technique that adapts
continuously and instantaneously to the environment. The algorithm, after
having identified the sea clutter, rejects this sea clutter from the data; thus
enhancing the probability of detecting small objects in a clutter environment.
The final step is the actual detection that makes use of the advantages of the
parametric representation. This results in a lower rate of false detections and
as a consequence the later stages, like clustering and tracking, receive a more
accurate input. The technique behind this algorithm uses recent developments in
parametric time series analysis and performs well in suppressing sea as well as
land clutter.
4 - Classification and Feature Selection with Fused
Conditionally Dependent Binary Valued Features
Robert S. Lynch, Jr., Naval Undersea Warfare Center, Newport, RI, USA
Peter K. Willett, University of Connecticut, Storrs, CT, USA
Abstract: In this paper, the Bayesian Data Reduction Algorithm (BDRA)
is compared to several neural networks to demonstrate classification
performance and feature selection for fused binary valued features, where the
statistical dependency (i.e., correlation or redundancy) between the relevant
features of each class is varied. The BDRA uses the probability of error,
conditioned on the training data, and a “greedy”approach (similar to
a backward sequential feature search) for reducing irrelevant features from the
data. Results are shown by plotting the probability of error as a function of
the conditional probability between adjacent relevant features, where the
number of relevant features is varied. In general, it is demonstrated that the
performance difference between the BDRA and the neural networks depends on the
statistical dependency between the features.
Session MoC2: Target Tracking -
1 – Track Fusion - 1
Chair: Chee-Yee Chong, Booz Allen & Hamilton, San
Francisco, CA, USA
Co-Chair: X.Rong Li, University of New Orleans, LA, USA
1- Evaluating Hierarchical Track Fusion with
Information Matrix Filter
Kuo-Chu Chang, George Mason University, Fairfax, VA, USA.
Abstract:This paper examines track fusion performance under various
degrees of non-deterministicitity of the target dynamics, i.e., process noises.
There are three approaches to state vector fusion, Weighted Covariance,
Information Matrix, and Pseudo-Measurement. This paper focuses on performance
evaluation of the Information Matrix form of state vector fusion. Closed form
analytical solution of steady state fused covariance for hierarchical fusion
architecture both with and without feedback have been derived. These results
provide interesting insight into the mechanism of track fusion and greatly
simplify the evaluation of fusion performance. In addition, availability of
such a solution facilitates the trade-off studies for designing fusion systems
under various operating conditions.
2 - Unified Optimal Linear Estimation Fusion - Part
I: Unified Models and Fusion Rules
X. Rong Li, University of New Orleans, LA, USA
Yunmin Zhu, Sichuan University, Chengdu, Sichuan, China
Chongzhao Han, Xi’an Jiaotong University, Xi'an, Shaanxi, China
Abstract: This paper deals with estimation fusion; that is, data
fusion for the purpose of estimation. Three estimation fusion architectures are
considered: centralized, distributed, and hybrid. A unified linear model and
general framework for these three architectures are established. Optimal fusion
rules in the sense of best linear unbiased estimation (BLUE) and weighted least
squares (WLS) are presented for cases with either complete, incomplete, or no
prior information. These rulesare muchmore generalandflexible
thanpreviousresults. For example, they are in a unified form that are optimal
for all the three fusion architectures with arbitrariy correlation of local
estimates or observation noises across sensors or across time. They are also in
explicit forms convenient for implementation.
3 - Unified Optimal Linear Estimation Fusion - Part
II: Discussions and Examples
X. Rong Li, University of New Orleans, LA, USA
Jie Wang, Xi’an Jiaotong University, Shaanxi, China
Abstract:Several unified optimal linear estimation/track fusion rules
in the sense of best linear unbiased estimation (BLUE) and weighted least
squares (WLS) have been presented in [3] for centralized, distributed, and
hybrid fusion architectures. This paper presents their relationships, verifies
these rules and demonstrate via computer simulation examples how these fusion
rules can be used in cases with either complete, incomplete, or no prior
information about the esti-matee (i.e., the quantity to be estimated).
4 - Problem Characterization in Tracking / Fusion
Algorithm Evaluation
Chee-Yee Chong, Booz Allen & Hamilton, San Francisco, CA, USA
Abstract:The performance of a tracking/fusion algorithm depends very
much on the complexity of the problem. This paper presents an approach for
evaluating tracking/fusion algorithm that considers the difficulty of the
problem. Evaluation is performed by characterizing the performance of the basic
functions of prediction and association. The problem complexity is summarized
by means of context metrics. Two context metrics to characterize prediction and
association difficulty are normalized target mobility and normalized target
density. These metrics should be presented along with the performance metrics
in performance evaluation. The metrics also allow more efficient generation of
input data for performance evaluation. Simple tests for basic tracking
algorithm functions are presented.
Session MoC3: Sensor and
Information Fusion
Chair: Jeffery Layne, US Air Force Research Lab. WPAFB, OH,
USA
Co-Chair: Erik Blasch, US Air Force Research Lab. WPAFB, OH, USA
1 - Robust Data Fusion
J.F. Grandin, M. Marques, Thomson-CSF DETEXIS, Trappes-Elancourt, France
Abstract:This paper compares different fusion processes in terms of
error probability and robustness. Simples fusion processes are studied in the
general framework of two binary sensors (maximum likelihood and logical fusion
functions like ‘or’, ‘and’…). In the sequel, the
comparison is extended to likelihood vectors adding other fusion processes (
Bayes or entropy criteria, T-norms, T-conorms, means operators). Finally,
different models of fusion processes combination informed with the imprecision
and the reliability of information sources are proposed and demonstrate to have
good robustness properties.
2 - Methods and Concepts for Air Situation Picture
Generation
Eric Shynar, Uri Degen, Advanced Technology Ltd., Tel-Aviv, Israel
Abstract: There is some confusion in terminology concerning
architectures and algorithms of Air Situation Picture Generation (ASPG).
Therefore, two aspects concerning the ASPG are distinguished: the method of
ASPG - Single or Multi Radar Tracking, and the concept - Distributed or
Central, - of ASPG within an Air Defense Region, consisting of several Control
and Reporting Centers. The evolution from Single Radar Tracking to Multi Radar
Tracking and from Distributed Concept to Central Concept is discussed.
3 - Fusion Method For Physical Systems Based On
Physical Laws
Nageswara S. V. Rao, David B. Reister, Jacob Barhen, Oak Ridge National
Laboratory, Oak Ridge, TN, USA
Abstract: We consider a physical system described by a set of
parameters. Each parameter is either measured by a number of sensors or
estimated by a set of computer programs that use sensor measurements. As a
result, the resultant parameter values could be widely varying. We propose a
fusion method that combines the measurements and estimators based on the
physical laws that relate the parameters. In comparison with the traditional
fusion problems, there isnotraining set that provides the actual parameter
values. Furthermore, since every parameter is measured or estimated, there are
noparameters whose actual values are known. We propose a fuser based on the
least violation of the physical laws that relate the parameters. Under certain
smoothness conditions on the physical law, we show the asymptotic convergence
of our method, and also derive distribution-free performance bounds based on
finite samples. We illustrate the effectiveness of this method for a practical
problem of fusing well-log data in methane hydrate exploration. For this
problem, data fusion method resulted in an order of magnitude improvement in
the accuracy compared to the best set of estimators for the key parameter of
porosity.
4 - A Statistical Overview of Recent Literature in
Information Fusion
L. Valet, Gilles Mauris, Philippe Bolon, LAMII / CESALP, Annecy, France
Abstract:The objective of this paper is to make a picture of the
recent articles published on information fusion. Indeed, a great number of
documents dealing with this technique are available in the literature. A
classification scheme including application fields, fusion goals, fusion system
architecture and mathematical tools is proposed. This overview of the last
three years allows to compute the article distribution into each class.
Finally, some elements of preliminary analysis of this classification are
drawn.
Session MoC4: Information
Modeling
Chair: Roger Reynaud, IEF / University Paris XI, Orsay,
France
Co-Chair: Amy L. Magnus, Intelligent Information AFRL/IFTD, NY, USA
1 - Object Hypothesis Support in the Context of
Knowledge-Based Fuzzy Possibilistic Fusion of Image Descriptions
Sotiris N. Raptis, S.G. Tzafestas, National Technical University of Athens,
Greece.
Abstract:In the paper presented here a possibilistic image fusion
scheme is investigated supported by a prior knowledge about the features’
distribution among objects-hypotheses. As a measure for possibilistic
considerations fuzzy reasoning is used. The fusion strategy adopted here is
original, in that individual features are studied separately instead of
studying them independently of their own statistical behavior, as it is common
in the literature. Moreover the possibilistic considerations are based on the
fuzzy modeling of the features, again deviating from what is commonly seen in
the literature which is adopting a feature vector logic. The investigation
subjects are not the objects but their individual features although the final
output favors specific object hypotheses. It is seen that applying a
fuzzy-possibilistic reasoning for individual features is more efficient than
considering objects as candidate hypotheses. Uncertainty is reduced when
knowledge is embedded into the scheme. Prior knowledge is provided by the way
the image descriptions are attributed to specific hypotheses. We therefore
integrate into our computations a series of image descriptors ranging from
skeleton components as edges, curves and closed contours to texture and image
moments. These are fuzzily modeled. Fuzzy descriptors modeling is grounded on
the idea that after applying a fuzzy classification or segmentation algorithm
all pixels of the image will pertain to more than one neighboring image
segments, prototypes or patterns. So any further computation on these pixels
inherit initial pixels’ uncertainty provided by the segmentation
procedure. Results can be interpreted as an object dependent feature behavior,
which in turn leads to increased confidence in the right object hypotheses.
2 - Inquisitive Pattern Recognition
Amy L. Magnus, Intelligent Information AFRL/IFTD, Rome, NY, USA
Steven C. Gustafson, Air Force Institute of Technology, WPAFB, OH, USA
Abstract: In nature, inquisitiveness is the drive to question, to
seek a deeper understanding, and to challenge assumptions. For the discrete
world of computers, inquisitive pattern recognition is the constructive
investigation and exploitation of conflict in information. Data fusion is a
fertile proving ground for inquisitive technologies. Multi-source, multi-modal
data inherently contain conflicting information. As data fusion incorporates
capabilities for situation assessment, strategies to identify and resolve
conflict become important. Inquisitive pattern recognition (IPR) is a
persistent, unsupervised learning capability whose concepts include
falsification---similar to the supervised learning technique of cross
validation---and the classification of confusion in feature space. Coupled with
knowledge base technologies, inquisitive pattern recognition enables a computer
to acquire new experiences.
3 - A Symbolic Neuro-fuzzy Collaborative Approach
for Inducing Knowledge in a Pharmacological Domain
M. Carmo Nicoletti, Arthur Ramer, University of New South Wales, Sydney,
Australia
M. Aparecida Nicoletti, Fac. Ciências Farmacêuticas, Universidade
de São Paulo, Brazil
Abstract: This paper discusses the experiments conducted with two
conceptually different machine learning systems in a pharmacological domain
related to the use of different excipients in drug production. It shows how a
symbolic system can be used, in a collaborative way, to help a neuro-fuzzy
system to induce a more appropriate set of fuzzy rules.
4 - Double Representation of Information and Hybrid
Combination for Identification Systems
Alain Nifle, Thomson-CSF/ISR, Massy, France
Roger Reynaud, IEF / Université Paris XI, Orsay, France
Abstract: In an increasing number of classification systems, a priori
and observations are both present as probabilistic and possibilistic continuous
distributions to represent information in the most accurate and reliable way.
We propose a method where information is simultaneously modeled in term of
probability and possibility and is combined in a hybrid manner, without
changing it in an entirely probabilistic form nor in an entirely possibilistic
form. It thus defines a compatibility mass function. To extend the mechanism of
validation windowing found in tracking algorithms, a possibilistic distribution
is associated with each continuous probabilistic distribution, and contributes
to build a mass function related to the validation-rejection of the
observation. Thanks to these hybrid mechanisms of combination, we build a
classifier respecting some constraints in the framework of evidence theory. In
the paper we describe it and discuss its properties.
Session MoC5: System
Design
Chair: Akira Namatame, National Defense Academy, Japan
1 - Control, Estimation and Abstraction in Fusion
Architectures: Lessons from Human Information Processing
Carl B. Frankel, Organizational Measurement and Engineering, San Francisco,
CA, USA
Mark D. Bedworth, Jemity / University of Central England, Birmingham, UK
Abstract:Human fusion offers important principles for the design of
machine fusion. The most important of these is the separability of control,
estimation and abstraction, without which competent real-time responsiveness
cannot be assured. Another is to separate intention from embodiment, so that
intentions can be flexibly embodied. Yet another, similar to human emotions, is
the use of positive feedback events and subsequent feed-forward to produce
timely responses to dynamic situations. Still another, leveraging the
trajectory of human cognitive development, is to control scope of processing by
layering data abstractions so that processing results can be immediately
apprehended by human users. By applying these principles, machine fusion can
both increase its competence and also increase the perception of its
competence, increasing the likelihood that the machine will be received and
treated as a trusted partner.
2- Information Fusion Method For System
Identification Based On Sensitivity Analysis
Jacob Barhen, Nageswara S. V. Rao, Oak Ridge National Laboratory, TN, USA
Abstract:We consider the identification of a parametrized
time-invariant non-linear plant using a smooth model such as a sigmoid
non-linear network. There is measurement noise associated with the plant
parameters as well as it's input and output. An initial plant model is obtained
by utilizing the domain-specific knowledge in terms of the fundamental plant
equations, which in general only partially capture the plant dynamics. Once the
initial model is fixed, measurements are collected on the plant parameters and
input/output. We show that the iid measurements can be fused with the initial
plant model by recomputing the parameters. The updated parameters yield a more
accurate identifier of the original plant both in parameter and input/output
space. The method is based on empirical versions of the closed-form solutions
derived in the nuclear engineering literature for an ideal version of the
problem based the sensitivity analysis. We show the asymptotic convergence of
our computational procedure as well as derive its finite sample results. We
illustrate the method using an identifier based on a sigmoid feedforward neural
network.
3 - Information Value Mapping for Fusion
Architectures
Timothy J. Peterson, Malur K. Sundareshan, University of Arizona, Tucson,
AZ, USA
Abstract: In assessing a fused sensor system, one considers the
quality of the system architecture most often by the capabilities of the
individual sensors, and the attributes of the fusion algorithm. Though it is
possible to evaluate system performance in an idealized context, and model
real-world perturbations as random disturbances, it may be advantageous to
treat predictable events as deterministic. Additionally, a-priori information
is not limited to constraints on the object of interest, but also can be
applied to the scene in which the object resides. In this paper, the role of
information value mapping in the design of efficient fusion architectures is
presented. This concept provides a development tool for general scene modeling
that attempts to capture the relative value of each sensor’s input related
to a given location in the sensed scene. Some general guidelines for the
development of information value maps will be outlined. Specific examples
illustrate the modeling and implementation methods. The effects of including
signal processing methods for modifying the sensor outputs, and hence the
information value maps, will be given a particular focus. The use of currently
popular image super-resolution algorithms is offered for an illustrative
discussion of these methods.
4 - Decentralized Decision Networks and Their
Emergent Properties
Saori Iwanaga, Akira Namatame, National Defense Academy, Yokosuka, Japan.
Abstract:We address a new concept of decentralized decision networks
that have a high degree of survivability when they are simultaneous software
errors, hardware malfunctions or hostile attacks. The large-scale effects of
locally interacting agents are called emergent properties of the system.
Emergent properties are often surprising because it can be hard to anticipate
the full consequences of even simple forms of interaction. We address a new
framework and architecture for a distributed decision system to aid real-time
dynamic decision-making in a highly competitive environment.
Session MoD1: Target Detection
and Recognition - 2
Chair: Per Svensson, Defence Research Establishment,
Stockholm, Sweden
Co-chair: Erland Jungert, Defence Research Establishment, Linköping,
Sweden
1 - Evolutionary Control of an Autonomous
Field
Mark W. Owen, SPAWAR, San Diego, CA, USA
Dale M. Klamer, Barbara Dean, Orincon Corporation, San Diego, CA, USA
Abstract: An autonomous field of sensor nodes needs to acquire and
track targets of interest traversing the field. Small detection ranges limit
the detectability of the field. As detections occur in the field, detections
are transmitted acoustically to a master node. Both detection processing and
acoustic communication drain a node’s power source. In order to maximize
field life, an approach must be developed to control processes carried out in
the field. In this paper we develop an adaptive threshold control scheme. This
technique will minimize the power consumption while still maintaining the
field-level probability of detection. The power consumption of the field of
sensor nodes is driven by the false alarm rate at the individual sensor nodes
in this problem formulation. The control law to be developed is based upon a
stochastic optimization technique known as evolutionary programming. At the end
of the paper, a set of results are presented with some conclusions.
2 - Target Detection and Identification using
Neural Networks and Multi-Agents Systems
Roger Cozien, C. Rosenberger, P. Eyherabide, J. Rossettini, A. Ceyrolle,
Saint-Cyr Coëtquidan Military School, Guer, France
Abstract: Our purpose is, in medium term, to detect in air images,
characteristic shapes and objects such as airports, industrial plants, planes,
tanks, trucks, … with great accuracy and low rate of mistakes. However, we
also want to value whether the link between neural networks and multi-agents
systems is relevant and effective. If it appears to be really effective, we
hope to use this kind of technology in other fields. That would be an easy and
convenient way to depict and to use the agents' knowledge which is distributed
and fragmented. After a first phase of preliminary tests to know if agents are
able to give relevant information to a neural network, we verify that only a
few agents running on an image are enough to inform the network and let it
generalize the agents' distributed and fragmented knowledge. In a second phase,
we developed a distributed architecture allowing several mutli-agents systems
running at the same time on different computers with different images. All
those agents send information to a "multi neural networks system"
whose job is to identify the shapes detected by the agents. The name we gave to
our project is Jarod.
3 - Using Optimal Variables for Bayesian Network
Classifiers
Fatima El-Matouat, Patrick Vannoorenberghe, Olivier Colot, Jacques Labiche,
Université / INSA de Rouen, Mont-Saint-Aignan, France
Abstract: Using graphical models to represent independence structure
in multivariate probability model has been studied since a few years. In this
framework, Bayesian networks have been proposed as an interesting approach for
uncertain reasoning. Within the framework of pattern recognition, many methods
of classification were developped based on statistical data analysis. Belief
networks were not considered as classifiers until the discovery that Naive
Bayes, a very simple kind of Bayesian network, is surprisingly effective. In
this paper, we propose to use belief networks classifiers with optimal
variables that is to say networks which have to manage discrete and continuous
variables.
4 - Modeling the Column Recognition Problem in
Tactical Information Fusion
Johan Björnfot, Per Svensson, Royal Institute of Technology, Stockholm,
Sweden.
Abstract: In this paper, we discuss the application of Hidden
Markov Modeling (HMM) techniques to the column recognition problem,
where a non-cooperative military unit consisting of a sequence of objects forms
a transportation column. Here the task is to infer the object composition and
organizational structure of the column from imperfect observations of
individual objects, in combination with generic a priori information about the
organizational structure of the non-cooperative forces. Good solution methods
for the column problem would provide a significant contribution to the
automatization of the aggregation process. To the best of our knowledge, the
application of HMM to the column problem has not been previously proposed.
Session MoD2: Target Tracking -
2 – Multi-Model Approach
Chair: X. Rong Li, University of New Orleans, LA, USA
Co-chair: Mohamad Farooq, Royal Military College, Kingston, Ontario, Canada
1 - Model-Set Adaptation Using a Fuzzy Kalman
Filter
Zhen Ding, Raytheon Systems Canada Limited, Waterloo, Ontario, Canada
Henry Leung, University of Calgary, Albert, Canada
Keith Chan, The Hong Kong Polytechnic University, Hong Kong
Abstract: In this paper, a fuzzy Kalman filter is proposed to combat
the model-set adaptation problem since it is found to be able to extract more
exactly dynamic information. The fuzzy Kalman filter uses a set of fuzzy rules
to adaptively control the noise covariance and that makes it more suitable for
real radar tracking. The proposed fuzzy Kalman filter is then combined with an
IMM algorithm, hence, a fuzzy IMM (FIMM) algorithm is obtained. The performance
of the FIMM algorithm is compared with that of an adaptive IMM (AIMM) algorithm
using real radar target tracking data. Simulation result shows that the FIMM
algorithm outperforms the AIMM algorithm in terms of both the root mean square
prediction error and the number of track loss.
2 - Tracking Closely Maneuvering Targets in Clutter
with an IMM-JVC Algorithm
Alexandre Jouan, Lockheed Martin Canada, Montréal, Québec,
Canada
Benoit Jarry, Hannah Michalska, McGill University, Montréal,
Québec, Canada
Abstract:The tracking of closely maneuvering targets represents a
challenge for both the contact-to-track association and the positional
estimation algorithms. Previous simulations have shown that the coupling of an
association scheme using the Jonker-Volgenant-Castanon (JVC) optimization with
an Interacting Multiple Model (IMM) positional estimator gives superior
tracking performance than other tested combinations such as the JVC-Adaptive
Kalman Filter (JVC-AKF), the Nearest Neighbor (NN)-AKF (NN-AKF) or the NN-IMM.
However, the efficiency of the JVC optimization scheme will depend on how the
assignment matrix is built. This highlights the role played by both the
construction of the buffers of contacts and the selection of the tracks likely
to be updated with the incoming contacts. After a brief recall of the IMM-JVC
formalism, this paper presents an analysis of the JVC output and identifies
additional functionalities that should be activated to improve its performance.
Simulation results are obtained on a scenario that involves two closely
maneuvering aircraft. Sensor reports are contaminated with randomly simulated
clutter.
3 - Comparing an Interacting Multiple Model
Algorithm and a Multiple-Process Soft Switching Algorithm: Equivalence
Relationship and Tracking Performance
Tan-Jan Ho, Mohamad Farooq, Royal Military College of Canada, Kingston,
Ontario, Canada
Abstract:In this paper, we show that a relationship exists between a
multiple-model soft- switching filter and an interacting multiple-model (IMM)
filter. By assuming that each model transition probability is equally likely,
the constraints imposed on the mixing and fusion weights in the proposed IMM
filter are similar to those in the multiple-model soft-switching filter
obtained in a referenced paper. Thus, the constraints derived from a
probabilistic approach can be reduced to those obtained via a deterministic
approach. Without the aid of an additional constraint, the IMM-type filter uses
the real-time information of the innovations and their covariances to choose
filter weights lying within 0 and 1. The results of this paper show that the
multiple-process soft-switching filter is a special case of an IMM filter. This
will be further substantiated through simulations.
4 - Variable- and Fixed- Structure Extended IMM
Algorithms Using Coordinated Turn Model
Emil Semerdjiev, Ludmila Mihaylova, Bulgarian Academy of Sciences, Sofia,
Bulgaria
X. Rong Li, University of New Orleans, LA, USA
Abstract:A new variable structure (VS) Extended Interacting Multiple
Model (EIMM) technique is developed in the paper. Fixed structure (FS) and VS
EIMM algorithms using extended constant velocity and extended coordinated turn
(ECT) models, are proposed. The ECT model includes the difference between the
unknown current turn rate and its fixed value assumed in each model for the IMM
algorithm. Due to the estimated turn rate, significant self-adjusting abilities
are provided to the designed EIMM algorithms, which give very good overall
accuracy and consistency. Both EIMM algorithms are compared to a particular VS
adaptive grid IMM algorithm. It is shown that the VS IMM algorithms show better
mobility, while the FS EIMM algorithm possesses better consistency.
Session MoD3: Image Fusion -
1
Chair: Pramod K. Varshney, Syracuse University, NY, USA
Co-chair: Wojciech Pieczynski, INT, Evry, France
1 - Different Focus Points Images Fusion Based on
Wavelet Decomposition
Xuan Yang, Wanhai Yang, Jihong Pei, Xidian University, Shaanxi, China
Abstract: A new technique is developed for the data fusion of two
images. Two spatially registered images with differing focus points are fused
by deciding clear objects. At first, an impulse function is defined to describe
the image quality of an object. Then the clear region is decided by analyze the
wavelet decomposition components of two primary images and two blurred images.
The results of the comparison show this method performing better in preserving
edge information for the test images than that of the other image fusion
methods.
2 - A Pyramid Approach For Multimodality Image
Registration Based On Mutual Information
Hua-mei Chen, Pramod K. Varshney, Syracuse University, NY, USA
Abstract:A pyramid approach for multimodality image registration
based on mutual information is presented. The image pyramid is obtained by
using the wavelet transform. An exhaustive search algorithm at the coarsest
level of the image pyramid is developed. Image partitioning and gray level
intensity binning are used to increase the fidelity of the process. Because of
the fact that image partitioning is used, our algorithm has the potential to be
parallelized and implemented on a multiprocessor computer. Our algorithm has
been applied on remotely sensed images. The results show that our algorithm is
very promising.
3 - 2-D Image Fusion by Multiscale Edge Graph
Combination
Stavri G. Nikolov, Dave R. Bull, C. Nishan Canagarajah, University of
Bristol, UK
Mike Halliwell, Peter N. T. Wells, Bristol General Hospital, UK
Abstract: A new framework for fusion of 2-D images based on their
multiscale edges is described in this paper. The new method uses the multiscale
edge representation of images proposed by Mallat and Hwang. The input images
are fused using their multiscale edges only. Two different algorithms for
fusing the point representations and the chain representations of the
multiscale edges (wavelet transform modulus maxima) are given. The chain
representation has been found to provide numerous new alternatives for image
fusion, since edge graph fusion techniques can be employed to combine the
images. The new framework studies different levels, i.e. pixel and feature
level, of image fusion in the wavelet domain.
4 - Robust Multisensor Image Registration with
Partial Distance Merits
Xiangjie Yang, Yunlong Sheng, Image Science Group, Univ. Laval, Ste-Foy,
Québec, Canada
Léandre Sévigny, DRE Valcartier, Courcelette, Québec,
Canada
Pierre Valin, Lockheed Martin Canada, Montréal, Québec, Canada
Abstract: Challenge in the registration of battlefield images in
visible and far-infrared bands is the feature inconsistency. We use a
contour-based approach and propose robust free-form curve-matching algorithms
using the adaptive hill climbing and the iterative closest point algorithm.
Both algorithms do not requires explicit curve feature correspondence, are
designed to be robust against outliers. The originality of this work is the use
of the mean partial distance as the objective function in the iterative closest
point algorithm, so that outliers can be easily handled by using rank order
statistics. A fast algorithm using the segment representation of Voronoi
diagram for the nearest point transform and the distance transform is used.
Session MoD4: Bayesian and
Belief Fusion Approaches
Chair: Wojciech Pieczynski, INT, Evry, France
Cochair: Mohammed Benjelloun, Université du Littoral Côte d'Opale,
Calais, France
1 - A Conservative Approach to Distributed Belief
Fusion
Churn-Jung Liau, Institute of Information Science, Academia Sinica, Taipei,
Taiwan
Abstract: In this paper, we develop logics for merging beliefs of
agents with different degrees of reliability. The logics are obtained by
combining the multi-agent epistemic logic and multi-sources reasoning systems.
Every ordering for the reliability of the agents is represented by a modal
operator, so we can reason with the merging information under different
situations. The approach is conservative in the sense that if an agent's belief
is in conflict with those of higher priorities, then his belief is completely
discarded from the merged result. We consider two strategies for the
conservative merging of beliefs. In the first one, if inconsistency occurs at
some level, then all beliefs at the lower levels are discarded simultaneously,
so it is called level cutting strategy. For the second one, only the level at
which the inconsistency occurs is skipped, so it is called level skipping
strategy. The formal semantics and axiomatic systems for these two strategies
are presented.
2 - A Generic Framework for Resolving the Conflict
in the Combination of Belief Structures
Eric Lefevre, Olivier Colot, Patrick Vannoorenberghe, Denis de Brucq,
Université/INSA de Rouen, France
Abstract: Within the framework of Dempster-Shafer theory of evidence,
the data fusion is based on the building of single belief mass by combination
of several mass functions resulting from distinct information sources. This
combination called Dempster's combination rule (or orthogonal sum) has several
interesting mathematical properties like commutativity or associativity.
Unfortunately, it badly manages the existing con ict between the various
information sources at the step of normalization. In this paper, we introduce
traditional combination operators used within the framework of evidence theory.
We propose other combination operators allowing an arbitrary redistribution of
the con icting mass on the propositions. These various combinations operators
were tested on sets of synthetic and real masses.
3 - Optimal Segmentation by Random Process
Fusion
Serge Reboul, Damien Brige, Mohammed Benjelloun, Université du
Littoral Côte d’Opale, Calais, France
Abstract: We introduce in this article an optimal segmentation method
of nonstationary random processes. Segmentation of a non stationary process
consists in assuming piecewise stationarity and in detecting the instants of
change. We consider here that all the data from all the sensors are avaible in
a same time and perform a global segmentation. The bayesian fusion method we
propose for the segmentation is based on the introduction of a joint prior
model for the simultaneously segmentation and estimation of data coming from a
set of sensors. We build a change process and define its prior distribution for
the data fusion. That allows us to propose the MAP estimate as well as some
minimum contraste estimate as a solution. We define, in the parametric
processes distribution case, the expression and signification of all the
segmentation’s parameters. We compare the performance of our detection
method in the case of two or three sensor. Application to the fusion of wind
data velocity and direction is proposed.
4 - Pairwise Markov Chains and Bayesian
Unsupervised Fusion
Wojciech Pieczynski, INT, Evry, France
Abstract: We propose a new model called a Pairwise Markov Chain
(PMC), which generalises the classical Hidden Markov Chain (HMC) model. The PMC
model is more general than HMC in that the process one wants to estimate is not
necessarily a Markov process. However, PMC allows one to use the classical
Bayesian restoration methods like Maximum A Posteriori (MAP), or Maximal
Posterior Mode (MPM). So, akin to HMC, PMC allows one to restore hidden
stochastic processes, with numerous applications to speech recognition,
multisensor image segmentation, among others. Furthermore, we propose a new
method of parameter estimation, which allows one to perform unsupervised
restoration with PMC. The method proposed is valid even with non Gaussian and
possibly correlated noise. Furthermore, the very form of the statistical
distribution of the noise need not be known exactly. All that is required is
that for each class the form of the noise distribution belongs to a given set
of forms.
Session MoD5: System Design and
Applications - Invited Session
Chair: Mark Bedworth, Jemity / University of Central
England, Birmingham, UK
1 - Data Fusion System Engineering
Alan N. Steinberg, Veridian ERIM International, Chantilly, VA, USA
Abstract: The paper reports on methods for cost-effective development
and integration of multi-sensor fusion technology. The methods presented derive
from the Project Correlation Data Fusion Engineering Guidelines with
significant evolution in current efforts for DARPA (Defense Advanced Research
Agency), BMDO (Ballistic Missile Defense Organization) and elements of the U.S.
intelligence community.
Approaches for four types of research are distinguished: Requirement-Driven
(Find solutions to mission problems) ; Acquisition-Driven (Prototype evaluation
of techniques to support system acquisition & technology insertion) ;
Phenomenology-Driven (Discover exploitation potential in untried combinations
of multi-sensor/ multi-discipline phenomenology ) ; Technology-Driven
(Proof-of-concept evaluation to support technology road-map).
Methods are presented for each of these. In particular, methods for
requirement-driven and acquisition-driven developments are discussed in terms
of the following phases: Problem decomposition - assigning the role for data
fusion, as well as for other system functions (sensors, communications,
response assets, human operators, etc.) ; Data Fusion Tree Design -
partitioning process among C3 nodes and into processing nodes; interaction with
sensors,/sources, resource management nodes, and information users ; Data
Fusion Node Design - data input/output, allocation to human/automatic
processes, technique selection ; Detailed Design and Development - pattern
application, algorithm tailoring, software adaptation and development ;
Multi-Sensor Test and Evaluation - metrics, test environments and procedures.
Diverse examples of system development experiences are presented, with lessons
learned regarding applicability of specific system engineering methods and data
fusion techniques.
2 - An Architecture for the Integration of All
Levels of Data Fusion
Stephane Paradis, Jean Roy, DRE Valcartier, Québec, Canada
Abstract: Data fusion is a key enabler for the situation analysis
process. The study and the integration of all data fusion levels require both a
suited architecture, and a modeling and simulation environment that allows the
technological demonstration of the situation analysis concepts to be
investigated, and also the demonstration of their supportive contribution to
the situational awareness state. This paper discusses both of these aspects. In
particular, it focuses on design issues related to development of a simulation
environment for the analysis of the tactical situation. Finally, the paper
describes a bootstrapping effort to develop a baseline of this environment.
This baseline has been successfully used to support the analysis of threat
evaluation algorithms for the command and control system of the IROQUOIS class
ships.
3 - Practical Trade-offs in Fusion Architecture
Design
Elisa Shahbazian, Rodney Hallsworth, Daniel Turgeon, Lockheed Martin Canada,
Canada
Abstract: Since 1991, the Research and Development (R&D) group at
Lockheed Martin Canada (LM Canada) has been developing and demonstrating the
application of Multi-Source Data Fusion (MSDF) techniques for target tracking
and identification within the Naval Command and Control (C2) for the HALIFAX
Class frigates. The current C2 as well as the sensor suite of the HALIFAX Class
were designed in the early 80s and based around a proprietary hardware
architecture. The sensor data is pre-processed and provided to the C2 in real
time. Considering the late 70s and 80s design of the sensor interfaces, where a
data fusion within the C2 was not a commonality, not all of the information
beneficial for a data fusion system is provided to the C2. After a sequence of
simulation and modelling efforts for an MSDF capability within the HALIFAX
Class C2, this project is now at a point where real data captured from a ship
trial on the HALIFAX Class is being injected into the MSDF. This is an on-going
activity and a number of iterations are foreseen before the MSDF becomes part
of HALIFAX Class C2. This paper provides a summary of lessons learned in this
exercise.
4 - Decisions in Condition Monitoring - An Exemplar
for Data Fusion Architecture
Paul Hannah, Andrew Starr, Andrew Ball, University of Mancheter, UK
Abstract: This paper aims to demonstrate the strategy and structures
involved in making decisions based on condition data, and to draw parallels
with data fusion models. A new df model is demonstrated, and examples are drawn
from condition monitoring applications. In particular, the work here presented
introduces a new framework for the application of data fusion solutions to the
analysis of engineering problems. A review of frameworks used in data fusion
applications is presented, along with important factors to consider in the lay
out of a robust process model, to host a coherent and effective data fusion
problem-solving strategy. The main theme of the work focuses on the development
of an intelligent multi-sensored engine. The partners involved in this research
effort aim to develop a robust methodology for sensing and analysis under harsh
environments, stressing its application to the fields of combustion and fault
diagnostics analysis. The proposed process model will be used to facilitate the
implementation of a common strategy to tackle these problems.
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