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