Last edited by Tejar
Friday, May 15, 2020 | History

5 edition of Multi-sensor fusion found in the catalog.

Multi-sensor fusion

fundamentals and applications with software

by R. R. Brooks

  • 132 Want to read
  • 24 Currently reading

Published by Prentice Hall PTR in Upper Saddle River, N.J .
Written in English

    Subjects:
  • Intelligent control systems.,
  • Multisensor data fusion.,
  • Application software.

  • Edition Notes

    Includes bibliographical references (p. 461-482) and index.

    StatementR.R. Brooks, S.S. Iyengar.
    ContributionsIyengar, S. S.
    Classifications
    LC ClassificationsTJ217.5 .B75 1998
    The Physical Object
    Paginationxvi, 488 p. :
    Number of Pages488
    ID Numbers
    Open LibraryOL682849M
    ISBN 100139016538
    LC Control Number97029569

    Multi-Sensor Data Fusion: An Introduction H.B. Mitchell. Categories: Mathematics\\Algorithms and Data Structures. Year: You can write a book review and share your experiences. Other readers will always be interested in your opinion of the books you've read. Whether you've loved the book or not, if you give your honest and detailed. Using MATLAB® examples wherever possible, Multi-Sensor Data Fusion with MATLAB explores the three levels of multi-sensor data fusion (MSDF): kinematic-level fusion, including the theory of DF; fuzzy logic and decision fusion; and pixel- and feature-level image fusion.

    Further, it discusses in detail the issues that arise when Kalman filtering technology is applied in multi-sensor systems and/or multi-agent systems, especially when various sensors are used in systems like intelligent robots, autonomous cars, smart homes, smart buildings, etc., requiring multi-sensor information fusion techniques. Multisensor Fusion book. Read reviews from world’s largest community for readers. For some time, all branches of the military have used a wide range of s Pages:

    The aim of this book is to present a suite of relevant concepts, tools, and methods of integrated multisensor data fusion and machine learning technologies to promote environmental sustainability. The process of machine learning for intelligent feature extraction consists of regular, deep, and fast learning algorithms. image fusion. He has published widely in the fields of image processing, fuzzy logic and sensor fusion and is the author of a recent textbook on data fusion (Multi-Sensor Data Fusion: An Introduction, Springer ).File Size: 16KB.


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Multi-sensor fusion by R. R. Brooks Download PDF EPUB FB2

The book reflects six years of sensor fusion research for the Office of Naval Research, introducing novel solutions to challenges such as image registration, distributed agreement, and sensor selection.

Multi-Sensor Fusion focuses extensively on applications, including neural networks, genetic algorithms, tabu search and Multi-sensor fusion book by: The book is intended to be self-contained. No previous knowledge of multi-sensor data fusion is assumed, although some familiarity with the basic tools of linear algebra, calculus and simple probability theory is by: Sensor Fusion - Foundation and Applications comprehensively covers the foundation and applications of sensor fusion.

This book provides some novel ideas, theories, and solutions related to the research areas in the field of sensor fusion. The book explores some of the latest practices and research works in the area of sensor fusion. The book contains chapters with Cited by: 6. The purpose of this book is to provide an introduction to the theories and techniques of multi-sensor data fusion.

The book has been designed as a text for a one-semester graduate course in multi-sensor data fusion. It should also be useful to advanced undergraduates in. Multi-sensor fusion in emotion recognition is useful for complementary information management (e.g. some emotions are better recognized by speech (sadness and fear) while others by facial expression (anger and happiness), and to achieve better performance and increased by: Multisensor data fusion is a technology to enable combining information from several sources in order to form a unified picture.

Data fusion systems are now widely used in various areas such as sensor networks, robotics, video and image processing, and intelligent system design, to Cited by: Multi-Sensor Data Fusion with MATLAB Written for scientists and researchers, this book explores the three levels of multi-sensor data fusion (MSDF): kinematic-level fusion, including the theory of DF; fuzzy logic and decision fusion; and pixel-level and feature-level image fusion.

Multi-Sensor Information Fusion. Xue-Bo Jin and Yuan Gao (Eds.) Pages: Published: March Your book will be printed and delivered directly from one of three print stations, allowing you to profit from economic shipping to any country in the world.

Generally we use Premium shipping with an estimated delivery time of business days. Direct fusion is the fusion of sensor data from a set of heterogeneous or homogeneous sensors, soft sensors, and history values of sensor data, while indirect fusion uses information sources like a priori knowledge about the environment and human input.

Sensor fusion is also known as (multi-sensor) data fusion and is a subset of information fusion. Introduction to multi-sensor data fusion Conference Paper (PDF Available) in Proceedings - IEEE International Symposium on Circuits and Systems - vol.6 June with 2, Reads.

The system is based on a multi-sensor fusion approach. It consists of a chest-worn device that embeds a controller board, an electrocardiogram (ECG) sensor, a temperature sensor, an accelerometer.

The aim of sensor fusion is to use the advantages of each to precisely understand its environment. The camera is a very good tool for detecting roads, reading signs or recognizing a vehicle. The Lidar is better at accurately estimating the position of this vehicle while the Radar is better at accurately estimating the speed.

Sensors, an international, peer-reviewed Open Access journal. Dear Colleagues, Research on multi-sensor fusion and sensor data analysis have made significant progress in both theoretical investigation and practical applications, in many fields, such as monitoring, operation, planning, control, and decision making of various environmental, structural, agricultural, food processing.

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 and computer science, as well as researchers and professional engineers. The book is intended to be self-contained.

Book Description. Using MATLAB ® examples wherever possible, Multi-Sensor Data Fusion with MATLAB explores the three levels of multi-sensor data fusion (MSDF): kinematic-level fusion, including the theory of DF; fuzzy logic and decision fusion; and pixel- and feature-level image fusion.

The authors elucidate DF strategies, algorithms, and performance evaluation mainly for. Multi-sensor fusion: fundamentals and applications with software. Abstract. No abstract available. Cited By. Ao B, Wang Y, Yu L, Brooks R and Iyengar S () On Precision Bound of Distributed Fault-Tolerant Sensor Fusion Algorithms, ACM Computing Surveys (CSUR),(), Online publication date: Jul The book examines the underlying principles of sensor operation and data fusion, the techniques and technologies that enable the process, including the operation of 'fusion engines'.

Fundamental theory and the enabling technologies of data fusion are presented in a systematic and accessible manner. the book. Although conceptually simple, the study of multi-sensor data fusion pre-sents challenges that are fairly unique within the education of the electrical engineer or computer scientist.

Unlike other areas encounted by a student of these subjects, multi-sensor data fusion draws on, and brings together,File Size: KB. Data fusion is a research area that is growing rapidly due to the fact that it provides means for combining pieces of information coming from different sources/sensors, resulting in ameliorated overall system performance (improved decision making, increased detection capabilities, diminished number of false alarms, improved reliability in various situations at hand) with Cited by: 8.

Description: 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 and computer science, as.

Using MATLAB examples wherever possible, Multi-Sensor Data Fusion with MATLAB explores the three levels of multi-sensor data fusion (MSDF): kinematic-level fusion, including the theory of DF; fuzzy logic and decision fusion; and pixel- and feature-level image fusion.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 .A Dempster-Shafer Method for Multi-Sensor Fusion I. Introduction In his book, Glenn Shafer introduced the Theory of Evidence, later referred to as the Dempster-Shafer Theory (DST).

This data fusion method is based on the idea of belief and as such can handle ignorance. According to Shafer, this theory was aCited by: 1.