Sensor and Data Fusion for Intelligent Transportation Systems
introduces readers to the roles of the data fusion processes
defined by the Joint Directors of Laboratories (JDL) data fusion
model and the Data Fusion Information Group (DFIG) enhancements,
data fusion algorithms, and noteworthy applications of data fusion
to intelligent transportation systems (ITS). Additionally, the
monograph offers detailed descriptions of three of the widely
applied data fusion techniques and their relevance to ITS (namely,
Bayesian inference, Dempster?Shafer evidential reasoning, and
Kalman filtering), and indicates directions for future research in
the area of data fusion. The focus is on data fusion algorithms
rather than on sensor and data fusion architectures, although the
book does summarize factors that influence the selection of a
fusion architecture and several architecture frameworks.
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