Multi-sensor data fusion involves combining information and data from multiple sources to obtain the most

accurate and specific assessment of an observed environment or situation [1-6]. Examples of data fusion

applications include monitoring the health of a complex machine [7], use of multiple sources to improve

medical diagnosis [8], automated target recognition for military applications [9], and environmental

monitoring [10]. Automated data fusion systems seek to emulate the ability of humans and animals to

improve their assessment of the external world by integrating information from multiple senses. Techniques

for automated data fusion range from signal processing and image processing techniques, to statistical based

estimation (e.g., Kalman filters), pattern recognition methods (cluster algorithms and adaptive neural

networks), voting, Bayesian and Dempster-Shafer methods, to automated reasoning techniques such as

expert systems, fuzzy logic, blackboard systems, and intelligent agents. An overview of these methods is

provided by Hall and McMullen [1] and Waltz and Llinas [2]. This chapter provides an introduction to

multi-sensor data fusion including the basic concepts, models, and processing architectures, algorithms and

techniques, and applications. A brief discussion of the state of the art of data fusion is provided at the end of

the chapter.