Final Long Program

Monday - Tuesday - Wednesday - Thursday

Thursday July 13

Session ThA1: Plenary talk

Hypermodality, Shift in Mode of Evaluation in Modal Logic

Prof. Dov Gabbay, King's College Strand, London, UK

Abstract: The author will discuss logical systems where a connective in a formula can shift meaning according to his place of occurence in the formula. In semantical terms this means that evaluation changes mode as one goes along. Such logics have wide range of applications from time action resource models to generalised quantifiers. A methodology is presented whereby any connective can be shifted (ie become a hyperconnective).

Session ThB1: Defense Systems

Chair: Stefan Arnborg, Royal Institute of Technology, Stockholm, Sweden
Co-Chair: John Kent, Hi-Q Systems Limited, Winchester, UK

1 - A Platform for Interoperable Fusion Models

Jean-François Challine, Thomson - TCC/SIC/SEA Colombes, France
Véronique Royer, ONERA, Châtillon, France

Abstract: The paper presents a generic and extensible prototype platform for Intelligence fusion. The fusion process is designed as an heterogeneous “algebra” of fusion operations. Each fusion operation works on given abstract data types. They are generic being independent of the semantics of data. Data and knowledge semantics is given explicitly by an input ontology together with descriptive models of the ontology concepts. The models are automatically encoded into representations suitable for the fusion operations. The approach allows to integrate external fusion algorithms, as well as to change the ontology. Interoperability is obtained through the common ontology : the consistent combination of fusion operations is indeed made possible because of a common semantics of data and common reference concepts, not because of common data and knowledge.

2 - Deploying Tactical Fusion Systems: the Challenges

John Kent, Hi-Q Systems Limited, Winchester, UK

Abstract: Although fusion tools have successfully been applied to a wide variety of problem domains, fusion research has failed to produce systems that can support the needs of the commanders of land-based military operations, especially those that involve operations other than war. This paper explores the reasons for this failure to date, summarising the lessons of recent operations and relevant NATO research, and shows that even the apparently simple task of training fusion system users can impose significant technical challenges. The paper concludes by suggesting an agenda for further research based on the development of man-in-the-loop systems consisting of appropriate decision support tools embedded in a wider application framework.

3 - Intelligent Threat Assessment Processor (ITAP) using Genetic Algorithms and Fuzzy Logic

Paul Gonsalves, Rachel Cunningham, Nick Ton, Dennis Okon, Charles River Analytics, Inc., Cambridge, USA

Abstract: The explosive growth in the area of information technology provides a tremendous opportunity for enhancing military warfighting capabilities. The management and processing of military intelligence information, the requisite assessment of enemy capabilities, intent, and objectives, and the generation of appropriate response recommendations form a critical element of battlespace operations. Here, we develop an Intelligent Threat Assessment Processor (ITAP) for enhancing tactical threat assessment. Our novel system integrates a genetic algorithm approach to predicting enemy courses of action (eCOAs), a fuzzy logic-based analysis of predicted eCOAs to infer enemy intent and objectives, and in conjunction with our on-going development of an Intelligent Fusion and Asset Management Processor (IFAMP), provides the necessary functionality to support multi-level data fusion. We see considerable potential for this approach in enhancing existing tactical decision-aiding systems and addressing future information dominated battlespace requirements.

4 - Information Awareness in Command and Control: Precision, Quality, Utility

Stefan Arnborg, Joel Brynielsson, Royal Institute of Technology, Stockholm, Sweden
Henrik Artman, Swedish Defence College, Stockholm, Sweden
Klas Wallenius, CelsiusTech Systems AB, Järfälla, Sweden

Abstract: In current Command and Control system design, the concept of information plays a central role. In order to find architectures for situation and threat databases making full use of all dimensions of information, the concept of information awareness must be understood. We consider and define some information attributes: measures of precision, quality and usability, and suggest some uses of these concepts. The analysis is Bayesian. A critical point is where subjective Bayesian probabilities of decision makers meet the objective sensor-related Bayesian assessments of the system. This interface must be designed to avoid credibility problems.

Session ThB2: Target Tracking - 6

Chair: Emil Semerdjiev, Bulgarian Academy of Sciences, Bulgaria
Co-Chair: Kuo-Chu Chang, George Mason University, Fairfax, VA, USA

1 - A Comparison of Fixed Gain IMM against two other Filters

Eric Derbez, Bruno Remillard, Université du Québec, Trois-Rivières, Québec, Canada
Alexandre Jouan, Lockheed Martin Canada, Montréal, Québec, Canada

Abstract: The purpose of this paper is to present an alternative to the constant acceleration Kalman filter which requires half the computational load and yet performs almost as well as the IMM filter. The theoretical justication for this filter came from a study of the IMM filter by two of the authors. The results of this study are recalled, and illustrative simulations using these filters are carried out by transforming noisy radar data into Cartesian coordinates and then applying a filter to each coordinate separately. The proposed filter is analysed against a constant velocity, constant acceleration IMM filter, and a constant acceleration regular Kalman filter. The stability properties of each of these filters are also addressed.

2 - Progressive Correction for Regularized Particle Filters

Nadia Oudjane, Christian Musso, ONERA, Châtillon, France

Abstract: Particle methods have been recently proposed to deal with the nonlinear filtering problem. These are Monte Carlo methods that can provide a nonparametric approximation to the signal conditional distribution even in nonlinear and non Gaussian cases, without depending on the state space dimension. In this article, we present a new version of regularized particle filter using a progressive correction (PC) principle which improves the approximation, in introducing a decreasing sequence of (fictitious) variance matrices for the observation noise. This method is applied to the multisensor tracking problem (radar and IR sensor) and compared to the classical regularized particle filter and the EKF.

3 - Optimal Single Sensor MHT Improvements

Huub W. de Waard, Hollandse Signaalapparaten B.V., The Netherlands

Abstract: The primary contribution of this paper is the definition of a marginal bearing density function for the measurements produced by a certain track which forms the theoretical foundation for the different proposed promising improvements. Stepwise implementation of the different measures can provide MHT applications with the means to use the available computer storage and computation time resources as eÆcient as possible. Furthermore, delay-times that can occur before processing/pruning are minimized.

Session ThB3: Security and Surveillance - 1

Chair: Robert H. Bishop, University of Texas, Austin, TX, USA
Co-Chair: Jean-Pierre Le Cadre, IRISA/CNRS, Rennes, France

1 - Decision Fusion using Support Vector Machines (SVM)

Bert Gutschoven, Patrick Verlinde, Royal Military Academy, Brussels, Belgium

Abstract: The contribution of this paper is twofold: (1) to formulate a decision fusion problem encountered in the design of a multi-modal identity verification system as a particular classification problem, (2) to propose to solve this problem by a Support Vector Machine (SVM). The multi-modal identity verification system under consideration is built of d modalities in parallel, each one delivering as output a scalar number, called score, stating how well the claimed identity is verified. A fusion module receiving as input the d scores has to take a binary decision: accept or reject identity. This fusion problem has been solved using Support Vector Machines. The performances of this fusion module have been evaluated and compared with other proposed methods on a multi-modal database, containing both vocal and visual modalities.

2 - A Temporal Fusion Algorithm for Multi-Sensor Tracking in Wide Areas

Olivier Wallart, C. Motamed, M. Benjelloun, Université du Littoral Côte d'Opale, Calais, France

Abstract: This article presents a distributed vision system for tracking of mobile objects over wide areas. A temporal data fusion is used in order to improve the decision making at the data association stage. The temporal fusion is performed with a possibilistic M.H.T. (Multiple Hypothesis Tracking). We have decided to use the Possibility Theory which handle efficiently uncertainties. The originality of this M.H.T. is the control of its development according to the online quality estimation of the data association based on a Necessity measurement.

3 - Information Fusion for Intrusion Detection

Nong Ye, Mingming Xu, Arizona State University, Tempe, AZ, USA

Abstract: There are many intrusion detection techniques existed. Each of them can produce the value of Indication and Warning (IW) to account for how serious the intrusion is. However, each technique reveals different aspect of the intrusion and has it’s own strength and weakness. An information fusion technique can be a relatively useful reference for detecting sophisticated and novel attacks. This paper presents three information fusion techniques starting from Artificial Neural Network(ANN) and linear regression , and finally to logistic regression. The performance of these systems is compared by testing them with data provided by DARPA Intrusion Detection Evaluation Program (1998).

4 - Integrated Vision and Sound Localization

Parham Aarabi, Safwat Zaky, University of Toronto, Ontario, Canada

Abstract: This paper illustrates the synergic advantages of a multi-modal object localization system utilizing vision and sound localization. Prototype vision and sound localization systems were developed and integrated using spatial probability maps, which allow any number of cameras or microphones with arbitrary orientation to be easily integrated. Test results show a significant improvement in the system’s ability to accurately localize objects in low signal to noise situations. Furthermore, the performance of the integrated system was shown to surpass that of the individual sub-systems.

Session ThB4: Detection Fusion 2

Chair: Peter K. Willett, University of Connecticut, Storrs, CT, USA
Co-Chair: Thiaglingam Kirubarajan, University of Connecticut, Storrs, CT, USA

1 - Global Optimization for Distributed and Quantized Bayesian Detection System

Ming Xiang, Chongzhao Han, Jiaotong University, Xi’an, China

Abstract: Global optimization of distributed detection system with multi-bit sensor output requires simultaneous solution of optimum fusion rule and of optimum quantizer mappings for individual sensors. For fixed sensor quantizer mappings, the optimal fusion rule can be easily shown to be a likelihood ratio test. But for a fixed fusion rule, the optimal quantizer mappings are very difficult to determine. In this paper, we consider the case of conditionally independent sensors. The optimal quantizer mappings for fixed fusion rule are derived, and optimal solution to the global optimization problem is obtained through a numerical algorithm.

2 - Effective Simplified Decentralized Target Detection in Multisensor Systems

Victor S. Chernyak, Scientific Research Institute of Radio Device Engineering, Moscow, Russia.

Abstract: Decentralized target detection optimization in multisensor systems, particularly in multisite radar systems, is usually reduced to the optimum choice of detection thresholds at all sensors and of a decision rule at the information fusion center. For each decision rule, optimum peripheral thresholds are calculated by solving a system of m+1 complicated nonlinear equations in m+1 unknowns where m is the number of sensors. Since optimum thresholds depend on signal-to-noise ratios, those equations are to be solved practically in real time. Such a procedure is very cumbersome and requires large computational resources. In this paper, an alternative simple and effective approach to the problem is described. The key idea is a "uniform distribution" of any given output false alarm probability between all sensors. Thus solving the above mentioned equations and "tuning" local false alarm probabilities in real time are avoided. The performance analysis has shown that energy losses are small (with respect to the optimum procedure) for both fixed threshold and CFAR peripheral detection. Therefore, the suggested approach may be recommended for practical use.

3 - Unified Fusion Rule in Multisensor Network Decision Systems

Yunmin Zhu, Department of Mathematics, Sichuan University, Chengdu, Sichuan, China
X. Rong Li, University of New Orleans, LA, USA
Zhisheng You, Sichuan University, Chengdu, Sichuan, China

Abstract: In this paper, we present a unified fusion rule in distributed multi-hypothesis multisensor network decision systems, where communication pattern between sensors is given and fusion center can also observe data. For the above decision networks, we propose a specific fusion rule, which is in fact of the most general form (i.e. other fusion rules are all its special cases) and independent of the statistical characteristics of observations and decision criteria. It is therefore called unified fusion rule of the decision network systems. To reach the globally optimal decision performance, people only need to optimize sensor rules under the unified fusion rule for given conditional distributions of observations and decision criterion. Computer simulations support the above results and show some guidance on how to assign sensors to nodes in a signal detection network with given communication pattern.

4 - Detection Fusion under Dependence

Peter K. Willett, University of Connecticut, Storrs, CT, USA
Peter Swaszek, University of Rhode Island, RI, USA
Rick Blum, Lehigh University, Bethlehem, PA, USA

Abstract: Most results about quantized detection rely strongly on an assumpt on of ndependence among random variables.With this assumption removed,little is known. Thus, in this paper, Bayes-optimal binary quantization for the detection of a shift in mean in a piar of dependent Gaussian random variables is studied. For certain problem parametrizations (meaning: the signals and correlation coefficient) optimal quantization is achievable via a single threshold applied to each observation – the same as under independence.In other cases one observation is best ignored,or is quantized with two thresholds; neither behavioris seen under independence. Further,and again in distinction from the case of independence,it is seen that in certain situations an XOR fusion rule is optimal, and in these cases the implied dec sion rule is bizarre.

Session ThB5: Data Fusion Systems Evaluation and Test-Beds - Invited Session

Chair: Uri Degen, Advanced Technology Ltd., Tel-Aviv, Israel
Co-Chair: Victor Remez, Advanced Technology Ltd., Tel-Aviv, Israel

1 - On Measures of Performance to Assess Sensor Fusion Effectiveness

A. Theil, L.J.H.M. Kester, TNO - Physics and Electronics Lab., The Hague, Netherlands
Éloi Bossé, DRE Valcartier, Val Bélair, Québec, Canada

Abstract: In this paper, measures of performance (MOP’s) with which the effectiveness of sensor fusion can be assessed are discussed. A series of objective measures related to track management statistics, to track quality and to reaction time is outlined. The applicability of several MOP’s is demonstrated for an examples in which radar and camera data are combined, showing the benefit of sensor fusion.

2 - Empirical Evaluation of Multi-Radar Tracking Systems

Ari J. Joki, Jouko Saikkonen, Finnish Air Force Headquarters, Tikkakoski, Finland

Abstract: The formal descriptions of data association and target-tracking algorithms are forbiddingly formidable for non-expert eyes. Evaluating performance of tracking systems commercially proposed, based on only mathematics, requires the work of several full-time experts. It is still doubtful if the supplier is really implementing what the formulae describe. A subjective evaluation based on benchmark scenarios is seen to be meaningful for non-technical decision-makers.

3 - A Test Bed for Sensor Suite and Multi Sensor Tracking Algorithms Studies

Uri Degen, Victor Remez, Advanced Technology Ltd., Tel-Aviv, Israel

Abstract: To specify and define a multi-sensor tracking (MST) system, the tasks of sensor suite definition and data fusion concepts and algorithms definition should be performed. Data Fusion Test Bed (DFTB) is designed to model the MST system and to enable simulation of target behavior (motion and emissions) and sensor detections with or without preprocessing, and implementation of data fusion and sensor management algorithms and their post-execution performance evaluation. DFTB outputs are scenarios, simulated targets and sensor detections (including potential detections), illumination statistics, intermediate data fusion results, final data fusion results and analysis results. These results can be presented in form of data files, tables, graphics and replay. DFTB can run multitude of scenarios automatically without operator interaction according to operator defined task list.

4 - Refinement of Targets Situation Picture Quality Evaluation Methodology

Leonid Shvartser, Victor Remez, Advanced Technology Ltd., Tel-Aviv, Israel

Abstract: Data Fusion (DF) performance evaluation is based on the association of “true” and estimated (by DF) Target Situation Pictures (TaSP), which is followed by a variety of Figures of Merit (FOM) calculations. However, because of TaSP configuration complexity, different approaches should be employed for evaluation of different target activities. Therefore, the decomposition of “true” TaSP into target activity patterns is a pre-condition for the meaningful DF performance evaluation. Consequently, only the relevant FOM are calculated for each particular activity.

Session ThC2: Target Tracking and Identification

Chair: Subhash Challa, University of Melbourne, Australia
Co-Chair: Christian Musso, ONERA, Châtillon, France

1 - Target Tracking and Identification Issues when Using Real Data

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 onboard the Naval Command and Control (C2) for of the Canadian Patrol Frigates (CPF). The current C2 as well as the sensor suite of the CPF were designed in the early 80s within a proprietary hardware architecture. The sensor data is pre-processed and provided to the C2 without much consideration of how the data should be combined within the C2. After a sequence of simulation and modelling efforts for an MSDF capability within the CPF C2, this project is now at a point when real data captured from a ship trial on CPF 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 CPF C2. This paper provides a summary of lessons learned in this exercise at this point.

2 - Target Recognition Using Fuzzy Fusion Classifier

Pingkui Hou, Xuejun Wang, Xizhi Shi, Shanghai JiaoTong Univ., Shanghai, China
Liangji Lin, Mingzhi Zhang, Scientific Test and Control Technology Institute, Dalian, China

Abstract: This paper makes available a feature fusion technique in underwater target recognition and proposes the necessary characteristics for fusion classifier design. Then, Fuzzy fusion classifier (FFC) is designed based on the characteristics presented. FFC presented here does not make any assumption about samples distribution and emphasizes mutual restraints among different classes as well as synthesizes patterns like combination operation in fuzzy logic. For the application of underwater target recognition, FFC can efficiently improve classification performance of recognition system by synthesizing features from multisensors.

3 - A Constructive Bayesian Approach for Vehicle Monitoring

Y. Xiang, V. Lesser, University of Massachusetts, Amherst, MA, USA

Abstract: A key componentof a vehicle monitoring system is uncertainty management. Bayesian networks (BN) have emerged as a normative and effective formalism for uncertain reasoning in many AI tasks. Since a priori modeling of the domain into a BN is impractical due to the vast interpretation space, the BN formalism has been considered inapplicable to this type of task. We propose a framework in which the BN formalism can be applied to vehicle monitoring. The framework explores domain decomposition, model separation, model approximation, model compilation and re-analysis. Experimental implementation demonstrated good performance at near-realtime.

4 - Target Tracking Incorporating Flight Envelope Information

Subhash Challa, University of Melbourne, Parkville, Victoria, Australia
Niklas Bergman, Linköping University, Sweden

Abstract: Target tracking in usually performed assuming the target under consideration is adequately modelled asa point target with no reference to constraints on its motion. However, in reality, target motion is constrained by achievable maximum speeds and accelerations. This is non-standard information and its incorporation into conventional tracking, while promising significant benefits, poses a signifi cant challenge in constructing the posterior probability density function (PDF) of target state. In this paper, the optimal Bayes' recursion for this posterior PDF is derived and an algorithm for implementing this recursion based on stochastic sampling techniques is presented. An illustrative example demonstrating the benefits of incorporating this information is also provided.

Session ThC3: Security and Surveillance - 2

Chair: Isabelle Bloch, ENST Paris, France

1 - Dynamic Service Definition in the Future Mixed Surveillance Environment

Christos M. Rekkas, Jean-Marc Duflot, Pieter van der Kraan, Eurocontrol, Brussels, Belgium
Jean-Claude Rassou, Thomson-CSF/ISR, Massy, France

Abstract: The future Surveillance environment is expected to be heterogeneous and include new types of Data Sources, such as the Mode S, ADS-Broadcast and ADS-Contract, in addition to the classical radar sensors (i.e. PSR, SSR). The new types of sensors are capable of transmitting aircraft derived data of high importance for the Air Traffic Management functions (such as the conflict detection and resolution tools, flight data processing etc.) which make use of Surveillance data. Various types of transmission characteristics for the aircraft derived data are foreseen (periodic, event driven etc.). This paper presents the work on the development of a prototype function which dynamically defines the services to be requested from the Surveillance Data Sources in the future Surveillance data fusion environment. The Dynamic Service Definition (DSD) function has a set of inputs, including sensor data, track data, geographical data etc., (reflecting the current Traffic and Environment Situation), as well as the requests from the Surveillance Users. On the basis of these inputs and defined operational criteria, the function dynamically defines the most appropriate set of contracts which will be requested from the Data Sources, in order to “optimise” the Traffic Situation Picture and satisfy the requests of the Users. The contracts will be requested in order to receive a user defined set of data items for a corresponding set of tracks at specified transmission characteristics.

2 - Sensor Modeling and Data Processing for Airport Simulation

Antonello Pasquarelli, Alenia Marconi Systems, Roma, Italy
Andrea Corsini, Andrea Garzelli, University of Siena, Italy

Abstract: This paper presents the SEEDS simulation environment for the evaluation of distributed traffic control systems. The description starts with a general overview of the simulator, targeted for airport surface traffic simulation, and then focuses on the sensor models implemented in the prototype. The surveillance function foreseen in a real Advanced Surface Movement Guidance and Control Systems (A-SMGCS) has been studied and modeled; suitable set of sensors and signal processing algorithms have been considered and their performances have been analyzed in order to be compliant with the application performance requirements defined by International Organizations. The paper shows the sensor module architecture, how the sensors have been modeled and how the software module has been implemented and integrated in the core simulator. The interactions with the other modules of the simulator and the exchanged messages are also described.

3 - Characterization of Mine Detection Sensors in Terms of Belief Functions and their Fusion, First Results

Nada Milisavljevi¢, Royal Military Brussels, Belgium and ENST Paris, France
Isabelle Bloch, ENST Paris, France
Marc Acheroy, Royal Military Academy, Brussels, Belgium

Abstract: In this paper, characterization of mine detection sensors in terms of belief functions and their fusion are presented. Need for fusion of mine detection sensors is discussed, and reasons for choosing Dempster-Shafer framework are pointed out, taking into account speciÝcity and sensitivity of the problem. This work is done in the scope of the HUDEM project, where three promising and complementary sensors are under analysis. These sensors are presented, and detail analysis is performed in case of fusing the data from them. A way for including in the model inÛuence of various factors on sensors and their results is discussed as well and will be further analyzed in the future. The application of the approach proposed in this paper is illustrated on the frequent case of detecting metallic objects, but the possibility for modifying it to some other situations exists.

Session ThC4: Target Detection

Chair: Chongzhao Han, Jiaotong University, Xi'an, China
Co-Chair: Alexander Tartakovsky, University of Southern California, Los Angeles, CA, USA

1 - Entropy Based Optimization for Binary detection Networks

Denis Pomorski, Université des Sciences et Technologies Lille I, Villeneuve d'Ascq, France

Abstract: This contribution deals with the binary detection networks optimization using an entropy based criterion. The optimization of an elementary detection component consists in applying a variable threshold on the likelihood ratio, which depends on a posteriori probabilities. A gradient algorithm is proposed in order to find this threshold. The optimization results of the elementary detection component using entropy and Bayes' criteria are compared : the proposed approach has a very interesting property of robustness with respect to rare events, or with respect to events for which a priori probabilities are uncertain. In particular, the obtained ROC curve does not recede from the ideal point.

2 - Implementation of Hough Transform as Track Detector

Kiril Alexiev, Bulgarian Academy of Sciences, Sofia, Bulgaria

Abstract: Hough Transform is a convenient tool for features extraction from images. In this paper an implementation of Hough Transform is onsidered for automatic track initiation in the surveillance radar space. The influence of Hough parameter space granularity upon probability of track detection is analyzed. Analytical expressions for probability of track initiation using Hough Transform are derived in the presence of normal distributed additive system noise, measurement noise and without any noises. A new parameter space structure, matching with measurement errors is proposed. The Monte Carlo simulation confirms received analytical result.

3 - Sequential Testing of Multiple Hypotheses in Distributed Systems

Alexander Tartakovsky, University of Southern California, Los Angeles, CA, USA
X. Rong Li, University of New Orleans, LA, USA

Abstract: It is supposed that there is a multisensor system in which each sensor tests a finite number of hypotheses in a sequential manner. Then the decisions are transmitted to a fusion center, which combines them to improve the performance of the system. First, we propose a local multihypothesis sequential test procedure which allows one to fix the probabilities of errors at specified levels and is asymptotically optimal for general statistical models in the sense of minimizing the expected sample size when the probabilities of errors vanish. We then construct two fusion rules : non-sequential and sequential. The first fusion rule waits until all the local decisions in all sensors are made and then fuses them. It is optimal in the sense of minimizing the average probability oferror (Bayes criterion) or the maximal probability of error (minimax criterion). In contrast, the sequential fusion rule fuses local decisions one by one in the order they are made, and at each stage decides whether to continue fusion or to stop and make a final decision. It has an advantage over the first rule in that it reduces the total time to make a final decision for a given average probability of error. An example of fusion of binary local decisions shows that the final decision can be made substantially more reliable even for a small number of sensors (3-5).

4 - Global Optimization for Distributed Detection System under the Constraint of Likelihood Ratio Quantizers

Ming Xiang, Chongzhao Han, Jiaotong University, Xi’an, China

Abstract: An optimal solution to global optimization problem usually can be obtained only for conditionally independent sensors. As for dependent sensors, although the necessary conditions for global optimization can be found, an optimal solution usually can not be obtained. Thus, for distributed detection systems consisting of dependent sensors, some suboptimal global optimization method need to be considered. In this paper, we consider this suboptimal global optimization problem for distributed and quantized Bayesian detection system. We constrain the sensor quantizers to be likelihood ratio quantizers, and optimize the system performance under this constraint.

Session ThC5: Recognition

Chair: André Ayoun, Thomson-CSF/ISR, Massy, France
Co-Chair: Michel Prenat, Thomson-CSF Optronique, Guyancourt, France

1 - Model Generation and Cooperation in On-Line Omni-Writer Handwritting Recognition

Lionel Prevost, Maurice Milgram, Université Paris VI, Paris, France

Abstract: In this paper, we introduce a new method for on-line character recognition based on the cooperation of two classifiers. The first one is a k-nearest-neighbor classifier, the second one is an evolutionary neural classifier. Several cooperation architectures (already tested in OCR but seldom in on-line recognition) are presented, from the easier (weigthed sum of both classifier outputs) to the most complicated (integrating neural network). The recognition improvement varies between 30% and 50% according to the merging strategy. We try to appreciate each method asset on recognition rate and speed. Results are presented on 52 different character classes (upper and lower case letters) and more than 50000 examples from UNIPEN database.

2 - Multiple Expert System Design by Combined Feature Selection and Probability Level Fusion

Fuad M. Alkoot, J. Kittler, University of Surrey, Guildford, UK

Abstract: We propose a novel design philosophy for expert fusion by taking the view that the design of individual experts and fusion cannot be solved in isolation. Each expert is constructed as part of the global design of a final multiple expert system. The design process involves jointly adding new experts to the multiple expert architecture and adding new features to each of the experts in the architecture. We evaluate the performance of different fusion strategies ranging from linear untrainable strategies like Sum and Modified Product to linear and nonlinear trainable strategies like logistic regression, single layer perceptron and radial basis function classifier. We investigate two distinct design strategies which we refer to as parallel and serial. In both cases we show that the proposed integrated design approach leads to improved performance.

3 - An Entropy Method For Multisource Data Fusion

Bienvenu Fassinut-Mombot, Jean-Bernard Choquel, Université du Littoral Côte d'Opale, Calais, France

Abstract: The present paper proposes a generic model of the multisource data fusion in the framework of the theory of information, with closer attention being given the different nature ofdata processed incommon cases. This model is then used to elaborate processing methods able to face specific problems that may arise when multisource systems are implemented to achieve functions like classification and pattern recognition, matching of ambiguous observations, estimation, detection or tracking. Crucial practical problems to data fusion are more specifically dealt with, such as information representation, appropriate combination processing and decision making. Some clues are given on the practical use and implementation of such an approach, for example, in the distributed estimation problem.

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