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洋書 | 技術書

Sensor and Data Fusion: A Tool for Information Assessment and Decision Making, Second Edition
商品コード: 9780819491336

Sensor and Data Fusion: A Tool for Information Assessment and Decision Making, Second Edition

販売価格(税込) 9,800 円
ポイント: 98 Pt
関連カテゴリ:

洋書 > 技術書

出版社別 > SPIE

個  数

カゴに入れる

Lawrence A. Klein
512 pages; Hardcover
2012/9/27
PM222

詳細

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.


List of Figures
List of Tables
Preface
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 andArchitectures
 3.1 Definition of data fusion
 3.2 Level 1 processing
  3.2.1 Detection, classification, and identification algorithms for data fusion
  3.2.2 State estimation and tracking algorithms for data fusion
 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
  3.6.1 Sensor-level fusion
  3.6.2 Central-level fusion
  3.6.3 Hybrid fusion
  3.6.4 Pixel-level fusion
  3.6.5 Feature-level fusion
  3.6.6 Decision-level fusion
 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 z-test for the population mean
 4.6 Tests with fixed significance level
 4.7 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
  6.4.1 Dempster's rule with empty set elements
  6.4.2 Dempster's rule when only singleton propositions are reported
 6.5 Comparison of dempster-shafer with Bayesian decision theory
  6.5.1 Dempster-Shafer - Bayesian equivalence example
  6.5.2 Dempster-Shafer - Bayesian computation time comparisons
 6.6 Probabilistic models for transformation of Dempster-Shafer belief functions for decision making
  6.6.1 Pignistic transferable belief model
  6.6.2 Plausibility transformation function
  6.6.3 Modified dempster-shafer rule of combination
  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
  7.5.1 Madaline
  7.5.2 Feedforward network
 7.6 Capacity of nonlinear classifiers
 7.7 Supervised and unsupervised learning
 7.8 Supervised learning rules
  7.8.1 LMS steepest descent algorithm
  7.8.2 LMS error correction algorithm
  7.8.3 Comparison of the LMS algorithms
  7.8.4 Madaline I and II error correction rules
  7.8.5 Perceptron rule
  7.8.6 Backpropagation algorithm
  7.8.7 Madaline III steepest descent rule
  7.8.8 Dead zone algorithms
 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
  8.2.1 Venn diagram representation of detection space
  8.2.2 Confidence levels
  8.2.3 Detection modes
 8.3 System detection probability
  8.3.1 Derivation of system detection and false alarm probability for nonnested confidence levels
  8.3.2 Relation of confidence levels to detection and false alarm probabilities
  8.3.3 Evaluation of conditional probability
  8.3.4 Establishing false alarm probability
  8.3.5 Calculating system detection probability
  8.3.6 Summary of detection probability computation model
 8.4 Application example without singleton sensor detection modes
  8.4.1 Satisfying the false alarm probability requirement
  8.4.2 Satisfying the detection probability requirement
  8.4.3 Observations
 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 braking 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
  9.6.1 Conventional mathematical solution
  9.6.2 Fuzzy logic solution
 9.7 Fuzzy logic applied to multitarget tracking
  9.7.1 Conventional kalman filter approach
  9.7.2 Fuzzy kalman filter approach
 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
  10.2.1 Coherent processing technique
  10.2.2 System design issues
 10.3 Angle data fusion
  10.3.1 Solution space for emitter locations
  10.3.2 Zero-one integer programming algorithm development
  10.3.3 Relaxation algorithm development
 10.4 Decentralized fusion architecture
  10.4.1 Local optimization of direction angle track association
  10.4.2 Global optimization of direction angle track association
 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
Preface

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