Using cooperative mobile agents to monitor distributed and dynamic environments
Information Sciences: an International Journal
Physical interaction in pervasive computing: formal modeling, analysis and verification
Proceedings of the 2009 international conference on Pervasive services
Resilient control systems: next generation design research
HSI'09 Proceedings of the 2nd conference on Human System Interactions
A flexible framework for multisensor data fusion using data stream management technologies
Proceedings of the 2009 EDBT/ICDT Workshops
Bayesian data fusion for smart environments with heterogenous sensors
Journal of Computing Sciences in Colleges
A recursive fusion filter for angular data
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Hierarchical multiple sensor fusion using structurally learned Bayesian network
WH '10 Wireless Health 2010
Conjugate mixture models for clustering multimodal data
Neural Computation
Relational preference rules for control
Artificial Intelligence
On the fusion of imprecise uncertainty measures using belief structures
Information Sciences: an International Journal
ACM Transactions on Reconfigurable Technology and Systems (TRETS)
Review: on the use of agent technology in intelligent, multisensory and distributed surveillance
The Knowledge Engineering Review
Application of mixture of experts to construct real estate appraisal models
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Collaborative redundant agents: modeling the dependences in the diversity of the agents' errors
MICAI'11 Proceedings of the 10th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
Wireless Communications & Mobile Computing
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This textbook provides a comprehensive introduction to the theories and techniques of multi-sensor data fusion. It is aimed at advanced undergraduate and first-year graduate students in electrical engineering and computer science, as well as researchers and professional engineers. The book is intended to be self-contained. No previous knowledge of multi-sensor data fusion is assumed, although some familiarity with the basic tools of linear algebra, calculus and simple probability theory is recommended. Although conceptually simple, the study of multi-sensor data fusion presents challenges that are unique within the education of the electrical engineer or computer scientist. To become competent in the field the student must become familiar with tools taken from a wide range of diverse subjects including: neural networks, signal processing, statistical estimation, tracking algorithms, computer vision and control theory. All too often the student views multi-sensor data fusion as a miscellaneous assortment of different processes which bear no relationship to each other. In this book the processes are described using a common statistical framework. As a consequence, the underlying pattern of relationships that exists between the different methodologies is made evident.