Multi-sensor data fusion (POD)

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. The book is illustrated with many real-life applications and contains an extensive list of modern references. It is accompanied by a webpage from which supplementary material may be obtained, including support for course instructors and links to relevant matlab code.

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.