Advances and Challenges in Multisensor Data and Information Processing

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Eric Lefebvre
IOS Press, 2007 - Computers - 401 pages
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Information fusion resulting from multi-source processing, often called multisensor data fusion when sensors are the main sources of information, is a relatively young (less than 20 years) technology domain. It provides techniques and methods for: Integrating data from multiple sources and using the complementarity of this data to derive maximum information about the phenomenon being observed; Analyzing and deriving the meaning of these observations; Selecting the best course of action; and Controlling the actions. Various sensors have been designed to detect some specific phenomena, but not others. Data fusion applications can combine synergically information from many sensors, including data provided by satellites and contextual and encyclopedic knowledge, to provide enhanced ability to detect and recognize anomalies in the environment, compared with conventional means. Data fusion is an integral part of multisensor processing, but it can also be applied to fuse non-sensor information (geopolitical, intelligence, etc.) to provide decision support for a timely and effective situation and threat assessment. One special field of application for data fusion is satellite imagery, which can provide extensive information over a wide area of the electromagnetic spectrum using several types of sensors (Visible, Infra-Red (IR), Thermal IR, Radar, Synthetic Aperture Radar (SAR), Polarimetric SAR (PolSAR), Hyperspectral...). Satellite imagery provides the coverage rate needed to identify and monitor human activities from agricultural practices (land use, crop types identification...) to defence-related surveillance (land/sea target detection and classification). By acquiring remotely sensed imagery over earth regions that land sensors cannot access, valuable information can be gathered for the defence against terrorism.

This books deals with the following research areas: Target recognition/classification and tracking; Sensor systems; Image processing; Remote sensing and remote control; Belief functions theory; and Situation assessment.

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Contents

Methods Applications Examples
1
Simulation of Distributed Sensor Networks
24
Joint Target Tracking and Classification via Sequential Monte Carlo Filtering
33
A Survey on Assignment Techniques
41
NonLinear Techniques in Target Tracking
48
Processing Sensor Network TDOAs with a Terascale Optical Core Device
56
On Quality of Information in MultiSource Fusion Environments
69
Polarimetric Features and Contextual Information Fusion for Automatic Target Detection and Recognition
78
A Few Application Examples
221
Secondary Application Wireless Technologies to Increase Information Potential for Defence Against Terrorism
236
Algorithms and System Design
243
Methods for Fused Image Analysis and Assessment
252
Object Tracking by Particle Filtering Techniques in Video Sequences
260
Wavelets Segmentation Pixel and Region Based Image Fusion
269
Methods and Examples
277
Information Management Methods in Sensor Networks
307

Enhancing Efficiency of Dynamic Threat Analysis for Combating and Competing Systems
85
Evidence Theory for Robust Ship Identification in Airborne Maritime Surveillance Missions
92
Improved Threat Evaluation Using Time of Earliest Weapon Release
99
Detection of Structural Changes in a Multivariate Data Using ChangePoint Models
106
Unification of Fusion Theories UFT
114
Belief Functions Theory for Multisensor Data Fusion
125
Limitations Practical Examples Variants Under Conflict and a New Adaptive Combination Rule
148
Decision Support and Information Fusion in the Context of Command and Control
157
Fusion in European SMART Project on Space and Airborne Mined Area Reduction
164
The DSmT Approach for Information Fusion and Some Open Problems
171
Multitarget Tracking Applications of DezertSmarandache Theory
179
A Tutorial
187
Automated Registration for Fusion of Multiple Image Frames to Assist Improved Surveillance and Threat Assessment
211
A Novel Method for Correction of Distortions and Improvement of Information Content in SatelliteAcquired Multispectral Images
315
Multisensor Data Fusion in the Processes of Weighing and Classification of the Moving Vehicles
324
A Review
331
Principles and Methods of Situation Assessment
339
Higher Level Fusion for Catastrophic Events
351
OntologyDriven Knowledge Integration from Heterogeneous Sources for Operational Decision Making Support
359
Evaluation of Information Fusion Techniques Part 1 System Level Assessment
366
Evaluation of Information Fusion Techniques Part 2 Metrics
375
Rapid and Reliable Content Based Image Retrieval
384
Subject Index
397
Author Index
401
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