Mathematical Techniques in Multi-sensor Data Fusion

By: David L. Hall

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Hardcover | 1 February 1992

At a Glance

Hardcover
320 Pages

Dimensions(cm)
24.77 x 16.51 x 1.91

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This invaluable reference offers the most comprehensive introduction available to the concepts of multisensor data fusion. It introduces key algorithms, provides advice on their utilization, and raises issues associated with their implementation. With a diverse set of mathematical and heuristic techniques for combining data from multiple sources, the book shows how to implement a data fusion system, describes the process for algorithm selection, functional architectures and requirements for ancillary software, and illustrates man-machine interface requirements an database issues.

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You Can Find This Book In

Non-Fiction Engineering & Technology Electronics & Communications Engineering Electronics Engineering Automatic Control Engineering Earth Sciences, Geography, Environment, Planning Geography Geographical Information Systems GIS & Remote Sensing Computing & I.T.Databases Mathematics Applied Mathematics

Computer Science Mathematical Theory of Computation Maths for Computer Scientists Science Science in General

Acknowledgments	p. vii
Preface	p. ix
Introduction to Multisensor Data Fusion	p. 1
Introduction	p. 1
Fusion Applications	p. 2
Senors and Sensor Data	p. 7
The Inference Hierarchy: Output Data	p. 16
A Data Fusion Model	p. 18
Benefits of Data Fusion	p. 22
Architectural Concepts and Issues	p. 27
References	p. 32
A Taxonomy of Algorithms for Multisensor Data Fusion	p. 35
Taxonomy Overview	p. 35
Taxonomy of Positional Fusion Algorithms	p. 37
Taxonomy of Identity Fusion Algorithms	p. 42
Taxonomy of Ancillary Support Algorithms	p. 50
Summary	p. 51
References	p. 51
Data Association	p. 53
Introduction	p. 53
A General Association Process	p. 59
Gating	p. 66
Association Metrics	p. 69
Assignment	p. 80
Summary	p. 86
References	p. 87
Estimation	p. 89
Introduction	p. 89
Overview of Estimation Alternatives	p. 92
Batch Estimation	p. 106
Sequential Estimation	p. 116
Covariance Error Estimation	p. 125
References	p. 129
Identity Declaration	p. 131
Identity Declaration and Pattern Recognition	p. 131
Feature Extraction	p. 138
Parametric Templates	p. 145
Cluster Analysis Techniques	p. 146
Adaptive Neural Networks	p. 153
Physical Models	p. 156
Knowledge-Based Methods	p. 158
References	p. 161
Decision-Level Identity Fusion	p. 163
Introduction	p. 163
Classical Inference	p. 167
Bayesian Inference	p. 173
Dempster-Shafer's Method	p. 179
Generalized Evidence Processing Theory	p. 187
Heuristic Methods for Identity Fusion	p. 190
Implementation and Trade-Offs	p. 192
References	p. 195
Knowledge-Based Approaches	p. 197
Introduction to Artificial Intelligence	p. 197
Overview of Expert Systems	p. 201
Implementation of Expert Systems	p. 225
Logical Templating Techniques	p. 241
References	p. 247
Implementing Data Fusion Systems	p. 251
Implementation Issues for Data Fusion Systems	p. 251
Architecture Selection	p. 252
Selection of Data Fusion Techniques	p. 257
Development of Ancillary Support Functions	p. 262
Test and Evaluation of Data Fusion Systems	p. 277
A Survey of Military Applications	p. 278
Perspectives and Comments	p. 288
References	p. 290
Index	p. 297
Table of Contents provided by Syndetics. All Rights Reserved.

Mathematical Techniques in Multi-sensor Data Fusion

At a Glance

Hardcover

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How to return your order

Defective items

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