rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Sensor and data fusion for intelligent transportation systems
  name:
    name part: Klein, Lawrence A.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Society of Photo-Optical Instrumentation Engineers
    role:
      role term: publisher.
  type of resource: text
  genre: bibliography
  genre: elektronické knihy
  genre: electronic books
  origin info:
    place:
      place term: wau
    date issued: 2019
    copyright date: 2019
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (254 pages)
  abstract: "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"--
  table of 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
  table of contents: 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.
  note: Lawrence A. Klein.
  note: Title from PDF title page (SPIE eBooks Website, viewed 2019-06-26).
  note: Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty
  note: Includes bibliographical references and index.
  subject:
    topic: Intelligent transportation systems
  subject:
    topic: Multisensor data fusion
  subject:
    topic: Motor vehicles
    topic: Automatic control
  subject:
    topic: Traffic congestion
  subject:
    topic: Algorithms
  subject:
    topic: Computer algorithms
  related item:
    identifier: (DLC) 2019001144
  related item:
    title info:
      title: SPIE Press monograph ; PM305
  identifier: 1510627650
  identifier: 9781510627659
  identifier: 9781510627642
  identifier: 1510627642
  identifier: 9781510627666
  identifier: 1510627669
  identifier: 9781510627673
  identifier: 1510627677
  identifier: https://proxy.k.utb.cz/login
  access condition: Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty
  record info:
    record content source: SPIES
    record creation date: 190626
    record change date: 20240717213016.0
    record identifier: kn-on1107275079
    language of cataloging:
      language term: eng