Multisensor data fusion is an emerging technology with important applications in both the military and civilian sectors, such as target recognition, robotics, medical diagnostics, and "smart" buildings. It draws on techniques from wide-ranging disciplines, including artificial intelligence, pattern recognition, and statistical estimation. This handbook is an up-to-date, comprehensive resource for data fusion system designers and researchers. Top experts in the field lead readers from a basic introduction and survey of data fusion technology to advanced mathematics and theory and to some very practical advice for systems implementers.
A complete reference on data fusion system development and implementation. Features a thorough introduction to terminology and models, as well as coverage of advanced techniques for data association, target tracking, and identification. DLC: Multisensor data fusion--Handbooks, manuals, etc.
Multisensor data fusion is an emerging technology applied to Department of Defense (DoD) areas such as automated target recognition (ATR), identification-friend-foe-neutral (IFFN) recognition systems, battlefield surveillance, and guidance and control of autonomous vehicles. Non-DoD applications include monitoring of complex machinery, environmental surveillance and monitoring systems, medical diagnosis, and smart buildings. Techniques for data fusion are drawn from a wide variety of disciplines, including signal processing, pattern recognition, statistical estimation, artificial intelligence, and control theory. The rapid evolution of computers, proliferation of micro-mechanical/electrical systems (MEMS) sensors, and the maturation of data fusion technology provide a basis for utilization of data fusion in everyday applications.
This book is intended to be a comprehensive resource for data fusion system designers and researchers, providing information on terminology, models, algorithms, systems engineering issues, and examples of applications. The book is divided into four main parts. Part I introduces data fusion terminology and models. Chapter 1 provides a general introduction to data fusion and terminology. Chapter 2 introduces the Joint Directors of Laboratories (JDL) data fusion process model, widely used to assist in understanding DoD applications. In Chapter 3, Jeffrey Uhlmann discusses the problem of multitarget, multisensor tracking and introduces the challenges of data association and correlation. Chapter 4, by Ed Waltz, introduces concepts of image and spatial data fusion, and in Chapter 5 Richard Brooks and Lynne Grewe describe issues of data registration for image fusion. Chapter 6, written by Richard Antony, discusses issues of data fusion focused on situation assessment and database management. Finally, in Chapter 7, Joseph Carl contrasts some approaches to combining evidence using probability and fuzzy set theory.