Data Fusion: Concepts and Ideas
This textbook provides a comprehensive introduction to the concepts and idea of multisensor data fusion.
It is an extensively revised second edition of the author's successful book: "Multi-Sensor Data Fusion:
An Introduction" which was originally published by Springer-Verlag in 2007.
The main changes in the new book are:
New Material: Apart from one new chapter there are approximately 30 new sections, 50 new examples and 100 new references. At the same time, material which is out-of-date has been eliminated and the remaining text has been rewritten for added clarity. Altogether, the new book is nearly 70 pages
longer than the original book.
Matlab code: Where appropriate we have given details of Matlab code which may be downloaded from the worldwide web. In a few places, where such code is not readily available, we have included Matlab code in the body of the text.
Layout. The layout and typography has been revised. Examples and Matlab code now appear on a gray background for easy identification and advancd material is marked with an asterisk.
The book is intended to be self-contained. No previous knowledge of multi-sensor data fusion is assumed, although some familarity with the basic tools of linear algebra, calculus and simple probability is recommended.
Although conceptually simple, the study of mult-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 contrast, in this book the processes are unified by 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 examples taken from a diverse range of applications and contains an extensive list of modern references.
What people are saying - Write a review
We haven't found any reviews in the usual places.
Common Representational Format
Sequential Bayesian Inference
Bayesian Decision Theory
Appendix A Background Material
Other editions - View all
AdaBoost algorithm Anal assume Bayesian inference biometric boosting Borda count calculate classifier Sm cluster combination common representational format Computer Conf corresponding covariance matrix data fusion system defined denote detection distance distribution dynamic time warping ensemble equation error following example illustrates fused fusion cell fusion node given gray-levels histogram IEEE IEEE Trans image registration input data input images Intell Kalman Filter Kriging likelihood function linear Gaussian Mach matching matlab matlab toolbox method mixture multi-sensor data fusion multiple mutual information Naive Bayes optical flow optimal outliers output Patt pixel posteriori pdf posteriori probability principal component analysis probabilistic probability density problem Proc recognition robust robust statistics samples sensor management sensor measurements sensor observations spatial spectral clustering statistical Table target threshold tion track-to-track fusion tracking training sets Dm transformation variable weights