Online resource; title from PDF title page (EBSCO, viewed July 22, 2015).

Includes bibliographical references and index.

Introduction to data fusion process -- Statistics, probability models and reliability : towards probabilistic data fusion -- Fuzzy logic and possibility theory-based fusion -- Filtering, target tracking and kinematic data fusion -- Decentralised data fusion systems -- Component analysis and data fusion -- Image algebra and image fusion / S. Sethu Selvi -- Decision theory and fusion -- Wireless sensor networks and multimodal data fusion -- Soft computing approaches to data fusion -- Appendix A. Some Algorithms and/or Their Derivations -- Appendix B. Other Methods of DF and Fusion Performance Evaluation Metrics -- Appendix C. Automatic Data Fusion -- Appendix D. Notes and Information on Data Fusion Software Tools -- Appendix E. Definitions of Sensor DF in Literature -- Appendix F. Some Current Research Topics in DF.

Fills the Existing Gap of Mathematics for Data FusionData fusion (DF) combines large amounts of information from a variety of sources and fuses this data algorithmically, logically and, if required intelligently, using artificial intelligence (AI). Also, known as sensor data fusion (SDF), the DF fusion system is an important component for use in various applications that include the monitoring of vehicles, aerospace systems, large-scale structures, and large industrial automation plants. Data Fusion Mathematics: Theory and Practice offers a comprehensive overview of data fusion, and provides a proper and adequate understanding of the basic mathematics directly related to DF. The material covered can be used for evaluation of the performances of any designed and developed DF systems. It tries to answer whether unified data fusion mathematics can evolve from various disparate mathematical concepts, and highlights mathematics that can add credibility to the data fusion process.

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