| Contents Notes |
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. 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. |
