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

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 sen...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Klein, Lawrence A.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Bellingham, Wash. : SPIE Press, ©2004.
Colección:SPIE monograph ; PM138.
Temas:
Signal processing > Digital techniques.
Multisensor data fusion.
Traitement du signal > Techniques numériques.
Fusion multicapteurs.
Datenfusion
Sensor
Sensoren.
Signaalverwerking.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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.

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