DATA FUSION
Description of book
Purchase
Table of Contents
Book Review
Software
Problems and Solutions
Figures
Handouts
Multi-Sensor Data Fusion by H.B. Mitchell (Springer-Verlag, 2007).
"This textbook provides a comprehensive introduction to the theories and techniques of multi-sensor data fusion. It is aimed at advanced undergraduate and first-year graduate students in electrical engineering or computer science, as well as researchers and professional engineers. The book is intended to be self-contained. No previous knowledge of multi-sensor data fusion and its theories and techniques is assumed, although some familiarity with the basic tools of linear algebra, calculus and simple probability theory is recommended.
Although conceptually simple, the study of multi-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 be 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 processes which bear no relationship to each other. In contrast, in this book the processes are described using a common statistical framework. As a consequence, the underlying pattern of relationships that exist between the different methodologies is made evident.
The book is illustrated with many real-life applications and contains an extensive list of modern references. It is accompanied by a webpage from which supplementary material may be obtained, including support for course instructors and links to relevant matlab code".
Purchase
The book may be purchased from Amazon and Springer (the publisher).
Table of contents
The book contains 14 chapters
- Introduction
- Sensors
- Architecture
- Common Representational Format
- Spatial alignment
- Temporal Alignment
- Sensor Value Normalization
- Bayesian Framework
- Parameter Estimation
- Robust Statistics
- Sequential estimation
- Bayesian inference
- Ensemble Learning
- Sensor Management
Book Review
Prof. Stanley Rotman (Ben-Gurion University, Be'er Sheva University, Israel)
kindly provided the following review of an early version of the book.
Software
The following is a list of all the matlab toolboxes and m-files which appear in the book. Included in the list are full details regarding the software and authors. With this information the user should find it possible to locate any given software using an appropriate internet search engine.
Problems and Solutions
The following is a list of problems which accompany the book. For each problem we have given the relevant section in the book where the solution of the problem may be found.
Figures (Under construction)
The following is a list of figures which appear in the book and may be useful to course instructors preparing a course on multi-sensor data fusion.
Handouts (Under construction)
The following is a list of handouts which may be useful to course instructors preparing a course on multi-sensor data fusion.
|
