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Sensor and data fusion : a tool for information assessment and decision making

Author: Lawrence A Klein
Publisher: Bellingham, Wash. : SPIE Press, ©2004.
Series: SPIE Press monograph, PM138.
Edition/Format:   Print book : EnglishView all editions and formats
Summary:
This book illustrates the benefits of sensor fusion by considering the characteristics of infrared, microwave, and millimeter-wave sensors, including the influence of the atmosphere on their performance. Applications that benefit from this technology include: vehicular traffic management, remote sensing, target classification and tracking- weather forecasting- military and homeland defense. Covering data fusion  Read more...
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Additional Physical Format: Online version:
Klein, Lawrence A.
Sensor and data fusion.
Bellingham, Wash. : SPIE Press, ©2004
(OCoLC)644978184
Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Lawrence A Klein
ISBN: 0819454354 9780819454355 0819483281 9780819483287
OCLC Number: 54536689
Description: xxii, 317 pages : illustrations ; 27 cm
Contents: Chapter 1. Introduction --
Chapter 2. Multiple sensor system applications, benefits, and design considerations --
2.1. Data fusion applications to multiple sensor systems --
2.2. Selection of sensors --
2.3. Benefits of multiple sensor systems --
2.4. Influence of wavelength on atmospheric attenuation --
2.5. Fog characterization --
2.6. Effects of operating frequency on MMW sensor performance --
2.7. Absorption of MMW energy in rain and fog --
2.8. Backscatter of MMW energy from rain --
2.9. Effects of operating wavelength on IR sensor performance --
2.10. Visibility metrics --
2.10.1. Visibility --
2.10.2. Meteorological range --
2.11. Attenuation of IR energy by rain --
2.12. Extinction coefficient values (typical) --
2.13. Summary of attributes of electromagnetic sensors --
2.14. Atmospheric and sensor system computer simulation models --
2.14.1. LOWTRAN attenuation model --
2.14.2. FASCODE and MODTRAN attenuation models --
2.14.3. EOSAEL sensor performance model --
2.15. Summary --
References. Chapter 3. Data fusion algorithms and architectures --
3.1. Definition of data fusion --
3.2. Level 1 processing --
3.3. Level 2, 3, and 4 processing --
3.4. Data fusion processor functions --
3.5. Definition of an architecture --
3.6. Data fusion architectures --
3.7. Sensor footprint registration and size considerations --
3.8. Summary --
References. Chapter 4. Classical inference --
4.1. Estimating the statistics of a population --
4.2. Interpreting the confidence interval --
4.3. Confidence interval for a population mean --
4.4. Significance tests for hypotheses --
4.5. The z-test for the population mean --
4.6. Tests with fixed significance level --
4.7. The t-test for a population mean --
4.8. Caution in use of significance tests --
4.9. Inference as a decision --
4.10. Summary --
References. Chapter 5. Bayesian inference --
5.1. Bayes' rule --
5.2. Bayes' rule in terms of odds probability and likelihood ratio --
5.3. Direct application of Bayes' rule to cancer screening test example --
5.4. Comparison of Bayesian inference with classical inference --
5.5. Application of Bayesian inference to fusing information from multiple sources --
5.6. Combining multiple sensor information using the odds probability form of Bayes' rule --
5.7. Recursive Bayesian updating --
5.8. Posterior calculation using multivalued hypotheses and recursive updating --
5.9. Enhancing underground mine detection with data from two noncommensurate sensors --
5.10. Summary --
References. Chapter 6. Dempster-Shafer evidential theory --
6.1. Overview of the process --
6.2. Implementation of the method --
6.3. Support, plausibility, and uncertainty interval --
6.4. Dempster's rule for combination of multiple sensor data --
6.5. Comparison of Dempster-Shafer with Bayesian decision theory --
6.6 Probabilistic models for transformation of Dempster-Shafer belief functions for decision making --
6.7. Summary --
References. Chapter 7. Artificial neural networks --
7.1. Applications of artificial neural networks --
7.2. Adaptive linear combiner --
7.3. Linear classifiers --
7.4. Capacity of linear classifiers --
7.5. Nonlinear classifiers --
7.6. Capacity of nonlinear classifiers --
7.7. Supervised and unsupervised learning --
7.8. Supervised learning rules --
7.9. Generalization --
7.10. Other artificial neural networks and processing techniques --
7.11. Summary --
References. Chapter 8. Voting logic fusion --
8.1. Sensor target reports --
8.2. Sensor detection space --
8.3. System detection probability --
8.4. Application example without singleton sensor detection modes --
8.5. Hardware implementation of voting logic sensor fusion --
8.6. Application example with singleton sensor detection modes --
8.7. Comparison of voting logic fusion with Dempster-Shafer evidential theory --
8.8. Summary --
References. Chapter 9. Fuzzy logic and fuzzy neural networks --
9.1. Conditions under which fuzzy logic provides an appropriate solution --
9.2. Illustration of fuzzy logic in an automobile antilock system --
9.3. Basic elements of a fuzzy system --
9.4. Fuzzy logic processing --
9.5. Fuzzy centroid calculation --
9.6. Balancing an inverted pendulum with fuzzy logic control --
9.7. Fuzzy logic applied to multitarget tracking --
9.8. Fuzzy neural networks --
9.9. Fusion of fuzzy-valued information from multiple --
sources --
9.10. Summary --
References. Chapter 10. Passive data association techniques for unambiguous location of targets --
10.1. Data fusion options --
10.2. Received-signal fusion --
10.3. Angle data fusion --
10.4. Decentralized fusion architecture --
10.5. Passive computation of range using tracks from a single sensor site --
10.6. Summary --
References. Chapter 11. Retrospective comments --
Appendix A. Planck radiation law and radiative transfer --
A.1. Planck radiation law --
A.2. Radiative transfer theory --
References --
Appendix B. Voting fusion with nested confidence levels --
Index.
Series Title: SPIE Press monograph, PM138.
Responsibility: Lawrence A. Klein.
More information:

Abstract:

In this edition, Dr. Klein has expanded and updated the information to incorporate recent approaches to data and sensor fusion. For example, classical inference is a new topic, and the chapter on  Read more...

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