Sensor and Data Fusion: A Tool for Information Assessment and Decision Making, 2nd Ed.

The information in the second edition of this volume has been substantially expanded and updated to incorporate recent approaches to sensor and data fusion, as well as additional application examples. A new chapter about data fusion issues associated with multiple-radar tracking systems has also been added. This chapter includes topics such as sensor registration requirements, Kalman filtering, and a discussion of interacting multiple models. As in the first edition, the book discusses the benefits of sensor fusion that accrue when sensors that operate with different phenomenologies or surveil separate volumes of space are used to gather signatures and data about objects or events in their field of view. Subject matter includes: (1) applications of multiple-sensor systems to vehicular traffic management, target classification and tracking, military and homeland defense, and battlefield assessment; (2) target, background, and atmospheric signature-generation phenomena and modeling; (3) the JDL data fusion model; (4) sensor fusion architectures; and (5) detailed descriptions of algorithms that combine multiple-sensor data from target identity and tracking data fusion architectures. Bayesian, Dempster-Shafer, artificial neural networks, fuzzy logic, voting logic, and passive data association techniques for unambiguous location of targets are among the data fusion techniques that are explored.