Multi-Sensor Data Fusion with MATLAB

570 Pages·2009·8.56 MB·English

by  Jitendra R. Raol

Multi-Sensor
Data Fusion
with
M
ATLAB®

Multi-Sensor
Data Fusion
with
M
ATLAB®
Jitendra R. Raol
Boca Raton London New York
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Library of Congress Cataloging-in-Publication Data
Raol, J. R. (Jitendra R.), 1947-
Multi-sensor data fusion with MATLAB / Jitendra R. Raol.
p. cm.
“A CRC title.”
Includes bibliographical references and index.
ISBN 978-1-4398-0003-4 (hardcover : alk. paper)
1. Multisensor data fusion—D ata processing. 2. MATLAB. 3. Detectors. I. Title.
TA331.R36 2010
681’.2—dc22 2009041607
Visit the Taylor & Francis Web site at
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The book is dedicated
in loving memory to
Professor P. N. Thakre
(M. S. University of Baroda, Vadodara),
Professor Vimal K. Dubey
(Nanyang Technological University, Singapore),
and
Professor Vinod J. Modi
(University of British Columbia, Canada)

Contents
Preface ...................................................................................................xix
Acknowledgments ................................................................................xxi
Author .................................................................................................xxiii
Contributors .........................................................................................xxv
Introduction .......................................................................................xxvii
Part I: Theory of Data Fusion and Kinematic-Level Fusion
(J. R. Raol, G. Girija, and N. Shanthakumar)
1. Introduction ..................................................................................................3
2. Concepts and Theory of Data Fusion .....................................................11
2.1 Models of the Data Fusion Process and Architectures .....................11
2.1.1 Data Fusion Models ....................................................................13
2.1.1.1 Joint Directors of Laboratories Model .......................13
2.1.1.2 Modifi ed Waterfall Fusion Model ..............................17
2.1.1.3 Intelligence Cycle–Based Model ................................18
2.1.1.4 Boyd Model ...................................................................19
2.1.1.5 Omnibus Model ...........................................................20
2.1.2 Fusion Architectures ..................................................................21
2.1.2.1 Centralized Fusion .......................................................21
2.1.2.2 Distributed Fusion .......................................................21
2.1.2.3 Hybrid Fusion ...............................................................22
2.2 Unifi ed Estimation Fusion Models and Other Methods ...................23
2.2.1 Defi nition of the Estimation Fusion Process ...........................24
2.2.2 Unifi ed Fusion Models Methodology ......................................25
2.2.2.1 Special Cases of the Unifi ed Fusion Models ............25
2.2.2.2 Correlation in the Unifi ed Fusion Models ................26
2.2.3 Unifi ed Optimal Fusion Rules ..................................................27
2.2.3.1 Best Linear Unbiased Estimation Fusion Rules
with Complete Prior Knowledge ...............................27
2.2.3.2 Best Linear Unbiased Estimation Fusion Rules
without Prior Knowledge ...........................................28
2.2.3.3 Best Linear Unbiased Estimation Fusion Rules
with Incomplete Prior Knowledge ............................28
2.2.3.4 Optimal-Weighted Least Squares Fusion Rule ........28
2.2.3.5 Optimal Generalized Weighted Least Squares
Fusion Rule ...................................................................29
vii
viii Contents
2.2.4 Kalman Filter Technique as a Data Fuser ...............................29
2.2.4.1 Data Update Algorithm...............................................30
2.2.4.2 State-Propagation Algorithm .....................................31
2.2.5 Inference Methods ......................................................................32
2.2.6 Perception, Sensing, and Fusion ...............................................32
2.3 Bayesian and Dempster–Shafer Fusion Methods ..............................33
2.3.1 Bayesian Method .........................................................................34
2.3.1.1 Bayesian Method for Fusion of Data from
Two Sensors ..................................................................36
2.3.2 Dempster–Shafer Method .........................................................38
2.3.3 Comparison of the Bayesian Inference Method and
the Dempster–Shafer Method ...................................................40
2.4 Entropy-Based Sensor Data Fusion Approach ...................................41
2.4.1 Defi nition of Information ..........................................................41
2.4.2 Mutual Information ....................................................................43
2.4.3 Entropy in the Context of an Image .........................................44
2.4.4 Image-Noise Index ......................................................................44
2.5 Sensor Modeling, Sensor Management, and Information Pooling .....45
2.5.1 Sensor Types and Classifi cation ...............................................45
2.5.1.1 Sensor Technology .......................................................46
2.5.1.2 Other Sensors and their Important Features
and Usages ....................................................................48
2.5.1.3 Features of Sensors ......................................................51
2.5.1.4 Sensor Characteristics ...............................................52
2.5.2 Sensor Management ...................................................................53
2.5.2.1 Sensor Modeling ..........................................................55
2.5.2.2 Bayesian Network Model ............................................58
2.5.2.3 Situation Assessment Process ....................................58
2.5.3 Information-Pooling Methods ..................................................60
2.5.3.1 Linear Opinion Pool ....................................................60
2.5.3.2 Independent Opinion Pool .........................................61
2.5.3.3 Independent Likelihood Pool .....................................61
3. Strategies and Algorithms for Target Tracking and Data
Fusion ...........................................................................................................63
3.1 State-Vector and Measurement-Level Fusion .....................................69
3.1.1 State-Vector Fusion .....................................................................70
3.1.2 Measurement Data–Level Fusion .............................................71
3.1.3 Results with Simulated and Real Data Trajectories ...............71
3.1.4 Results for Data from a Remote Sensing Agency with
Measurement Data–Level Fusion .............................................72
3.2 Factorization Kalman Filters for Sensor Data Characterization
and Fusion ................................................................................................73
3.2.1 Sensor Bias Errors .......................................................................73
Contents ix
3.2.2 Error State-Space Kalman Filter ...............................................75
3.2.3 Measurement and Process Noise Covariance
Estimation ....................................................................................76
3.2.4 Time Stamp and Time Delay Errors .........................................77
3.2.5 Multisensor Data Fusion Scheme .............................................77
3.2.5.1 UD Filters for Trajectory Estimation .........................80
3.2.5.2 Measurement Fusion ...................................................81
3.2.5.3 State-Vector Fusion .......................................................82
3.2.5.4 Fusion Philosophy ........................................................82
3.3 Square-Root Information Filtering and Fusion in
Decentralized Architecture ...................................................................86
3.3.1 Information Filter ........................................................................87
3.3.1.1 Information Filter Concept .........................................87
3.3.1.2 Square Root Information Filter Algorithm ..............88
3.3.2 Square Root Information Filter Sensor Data Fusion
Algorithm .....................................................................................88
3.3.3 Decentralized Square Root Information Filter .......................89
3.3.4 Numerical Simulation Results ..................................................91
3.4 Nearest Neighbor and Probabilistic Data Association Filter
Algorithms ...............................................................................................93
3.4.1 Nearest Neighborhood Kalman Filter .....................................94
3.4.2 Probabilistic Data Association Filter ........................................96
3.4.3 Tracking and Data Association Program for
Multisensor, Multitarget Sensors .............................................97
3.4.3.1 Sensor Attributes..........................................................99
3.4.3.2 Data Set Conversion .....................................................99
3.4.3.3 Gating in Multisensor, Multitarget ..........................100
3.4.3.4 Measurement-to-Track Association .........................100
3.4.3.5 Initiation of Track and Extrapolation of Track .......101
3.4.3.6 Extrapolation of Tracks into Next Sensor Field
of View .........................................................................101
3.4.3.7 Extrapolation of Tracks into Next Scan ...................102
3.4.3.8 Track Management Process ......................................102
3.4.4 Numerical Simulation ..............................................................103
3.5 Interacting Multiple Model Algorithm for Maneuvering
Target Tracking .....................................................................................106
3.5.1 Interacting Multiple Model Kalman Filter Algorithm ........106
3.5.1.1 Interaction and Mixing .............................................108
3.5.1.2 Kalman Filtering ........................................................108
3.5.1.3 Mode Probability Update ..........................................109
3.5.1.4 State Estimate and Covariance Combiner ..............109
3.5.2 Target Motion Models ...............................................................110
3.5.2.1 Constant Velocity Model ............................................110
3.5.2.2 Constant Acceleration Model ....................................110