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
    sub title: a tool for information assessment and decision making
  name:
    name part: Klein, Lawrence A.
    role:
      role term: creator
  type of resource: text
  genre: bibliography
  genre: elektronické knihy
  genre: electronic books
  origin info:
    place:
      place term: wau
    place:
      place term: Bellingham, Wash
    publisher: SPIE Press
    date issued: ©2004
    date issued: 2004
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (xxii, 317 pages) : illustrations.
  abstract: 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.
  table of contents: 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.
  table of contents: 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.
  table of contents: 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.
  table of contents: 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.
  table of contents: 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 -- 6.5. 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.
  table of contents: 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.
  table of contents: 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.
  table of contents: 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.
  table of contents: 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.
  table of contents: 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.
  note: Lawrence A. Klein.
  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: Signal processing
    topic: Digital techniques
  subject:
    topic: Multisensor data fusion
  related item:
    title info:
      title: Sensor and data fusion
    name:
      name part: Klein, Lawrence A.
    origin info:
      publisher: Bellingham, Wash. : SPIE Press, ©2004
    identifier: (DLC) 2004003963
    identifier: (OCoLC)54536689
  related item:
    title info:
      title: SPIE monograph ; PM138
  identifier: 9781615837243
  identifier: 1615837248
  identifier: 9780819454355
  identifier: 0819454354
  identifier: 9780819481115
  identifier: 0819481114
  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: KNOVL
    record creation date: 110117
    record change date: 20240717213016.0
    record identifier: kn-ocn697185227
    language of cataloging:
      language term: eng