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Sensor and data fusion for intelligent transportation systems
Author
Title
Sensor and data fusion for intelligent transportation systems / Lawrence A. Klein.
ISBN
1510627650
9781510627659
9781510627642
1510627642
9781510627666
1510627669
9781510627673
1510627677
Publication
Bellingham, Washington, USA : SPIE Press, [2019]
Copyright Notice Date
©2019
Physical Description
1 online resource (254 pages)
Local Notes
Access is available to the Yale community.
Notes
Title from PDF title page (SPIE eBooks Website, viewed 2019-06-26).
Access and use
Access restricted by licensing agreement.
Summary
"Sensor and Data Fusion for Intelligent Transportation Systems introduces readers to the roles of the data fusion processes defined by the Joint Directors of Laboratories (JDL) data fusion model, data fusion algorithms, and noteworthy applications of data fusion to ITS. Additionally, the monograph offers detailed descriptions of three of the widely applied data fusion techniques and their relevance to ITS (namely, Bayesian inference, Dempster-Shafer evidential reasoning, and Kalman filtering), and indicates directions for future research in the area of data fusion. The focus is on data fusion algorithms rather than on sensor and data fusion architectures, although the book does summarize factors that influence the selection of a fusion architecture and several architecture frameworks"-- Provided by publisher
Variant and related titles
Knovel. OCLC KB.
Other formats
Print version:
Format
Books / Online
Language
English
Added to Catalog
August 07, 2023
Series
SPIE Press monograph ; PM305
Bibliography
Includes bibliographical references and index.
Contents
Preface
Acronyms
1. Introduction: 1.1. Applications to ITS; 1.2. Data, information, and knowledge; 1.3. Summary of book contents
2. Sensor and data fusion in traffic management: 2.1. What is meant by sensor and data fusion? 2.2. Sensor and data fusion benefits to traffic management; 2.3. Data sources for traffic management applications; 2.4. Sensor and data fusion architectures; 2.5. Detection, classification, and identification of a vehicle; 2.6. The JDL and DFIG data fusion models; 2.7. Level 1 fusion: detection, classification, and identification algorithms; 2.8. Level 1 fusion: state estimation and tracking algorithms; 2.9. Data fusion algorithm selection; 2.10. Level 2 and level 3 fusion processing; 2.11. Level 4 fusion processing; 2.12. Level 5 fusion processing; 2.13. Applications of sensor and data fusion to ITS; 2.14. Summary
3. Bayesian inference for traffic management: 3.1 Bayesian inference; 3.2 Derivation of Bayes' theorem; 3.3 Likelihood function and prior probability models; 3.4 Monty Hall problem; 3.5 Application of Bayes' theorem to cancer screening; 3.6 Bayesian inference in support of data fusion; 3.7 Bayesian inference applied to vehicle identification; 3.8 Bayesian inference applied to freeway incident detection using multiple-source data; 3.9 Bayesian inference applied to truck classification; 3.10 Causal Bayesian networks; 3.11 Summary
4. Dempster-Shafer evidential reasoning for traffic management: 4.1. Overview of the process; 4.2. Implementation of the method; 4.3. Support, plausibility, and uncertainty interval; 4.4. Dempster's rule for combining multiple-sensor data; 4.5. Vehicle detection using Dempster-Shafer evidential reasoning; 4.6. Singleton proposition vehicle detection problem solved with Bayesian inference; 4.7. Constructing probability mass functions; 4.8. Decision support system application of Dempster-Shafer reasoning; 4.9. Comparison with Bayesian inference; 4.10. Modifications to the original Dempster-Shafer method; 4.11. Summary
5. Kalman filtering for traffic management: 5.1. Optimal estimation; 5.2. Kalman filter application to object tracking; 5.3. State transition model; 5.4. Measurement model; 5.5. The discrete-time Kalman filter algorithm; 5.6. Relation of measurement-to-track correlation decision to the Kalman gain; 5.7. Initialization and subsequent recursive operation of the Kalman filter; 5.8. The a-b filter; 5.9. Kalman gain control methods; 5.10. Noise covariance values and filter tuning; 5.11. Process noise covariance matrix models
6. State of the practice and research gaps: 6.1. Data fusion state of the practice; 6.2. Need for continued data fusion research; 6.3. Prerequisite information for level 1 object assessment algorithms
Appendix: The fundamental matrix of a fixed continuous-time system
Index.
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