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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.
Introduction.- Sensors.- Architecture.- Common Representational Format.- Spatial Alignment.- Temporal Registration.- Sensor Value Normalization.- Bayesian Inference.- Parameter Estimation.- Robust Parameter Estimation.- Sequential Bayesian Inference.- Bayesian Decision Theory.- Multiple Classifier Systems.- Multi-Sensor Management.