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Sensor and data fusion for intelligent transportation systems

Author: Lawrence A Klein; Society of Photo-optical Instrumentation Engineers,
Publisher: Bellingham, Washington, USA : SPIE Press, [2019] ©2019
Series: SPIE Press monograph, PM305.
Edition/Format:   eBook : Document : EnglishView all editions and formats
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,  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
(DLC) 2019001144
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Lawrence A Klein; Society of Photo-optical Instrumentation Engineers,
ISBN: 9781510627659 1510627650
OCLC Number: 1107275079
Description: 1 online resource (254 pages).
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.
Series Title: SPIE Press monograph, PM305.
Responsibility: Lawrence A. Klein.

Abstract:

Introduces readers to the roles of the data fusion processes defined by the Joint Directors of Laboratories data fusion model and the Data Fusion Information Group enhancements, data fusion  Read more...

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