Sensor and data fusion [electronic resource] : a tool for information assessment and decision making / Lawrence A. Klein.

Format:

Book

Author/Creator:

Klein, Lawrence A.

Contributor:

Society of Photo-Optical Instrumentation Engineers.

Series:

SPIE Press monograph ; PM138.

SPIE Press monograph ; PM138

Language:

English

Subjects (All):

Signal processing--Digital techniques.

Signal processing.

Multisensor data fusion.

Physical Description:

1 online resource (362 p.)

Place of Publication:

Bellingham, Wash. : SPIE Press, 2004 (2010 printing)

Language Note:

English

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 algorithms in detail, Klein includes a summary of the information required to implement each of the algorithms discussed, and outlines system application scenarios that may limit sensor size but that require high resolution data.

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

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

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

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

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

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

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

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

Chapter 11. Retrospective comments

Appendix A. Planck radiation law and radiative transfer

A.1. Planck radiation law

A.2. Radiative transfer theory

Appendix B. Voting fusion with nested confidence levels

Index.

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

ISBN:

1-61583-724-8

0-8194-8111-4

OCLC:

606697056