MARC Record from marc_columbia

LEADER: 06603cam a2200769Mi 4500
001 16626992
005 20220703224351.0
006 m eo d
007 cr bn||||m|||a
008 190626t20192019wau ob 001 0 eng d
010 $z 2019001144
035 $a(OCoLC)on1107275079
035 $a(NNC)16626992
040 $aSPIES$beng$erda$cSPIES$dOCLCO$dCUS$dUIU$dOCLCF$dUPM$dOCLCQ$dDKU$dUWW$dYDX$dEBLCP$dOCLCO
019 $a1122891281$a1231605196
020 $a9781510627659$q(PDF)
020 $a1510627650$q(PDF)
020 $z9781510627642$q(softcover)
020 $z1510627642$q(softcover)
020 $z9781510627666$q(ePub)
020 $z1510627669$q(ePub)
020 $z9781510627673$q(Kindle)
020 $z1510627677$q(Kindle)
024 7 $a10.1117/3.2525400$2doi
035 $a(OCoLC)1107275079$z(OCoLC)1122891281$z(OCoLC)1231605196
050 4 $aTE228.3$b.K537 2019
082 04 $a388.3/12$223
049 $aZCUA
100 1 $aKlein, Lawrence A.,$eauthor.
245 10 $aSensor and data fusion for intelligent transportation systems /$cLawrence A. Klein.
264 1 $aBellingham, Washington, USA :$bSPIE Press,$c[2019]
264 4 $c©2019
300 $a1 online resource (254 pages)
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
490 1 $aSPIE Press monograph ;$vPM305
504 $aIncludes bibliographical references and index.
505 0 $aPreface -- 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
505 8 $a4. 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.
520 $a"Sensor and Data Fusion for Intelligent Transportation Systems introduces readers to the roles of the data fusion processes defined by the Joint Directors of Laboratories (JDL) data fusion model, data fusion algorithms, and noteworthy applications of data fusion to ITS. Additionally, the monograph offers detailed descriptions of three of the widely applied data fusion techniques and their relevance to ITS (namely, Bayesian inference, Dempster-Shafer evidential reasoning, and Kalman filtering), and indicates directions for future research in the area of data fusion. The focus is on data fusion algorithms rather than on sensor and data fusion architectures, although the book does summarize factors that influence the selection of a fusion architecture and several architecture frameworks"--$cProvided by publisher
500 $aTitle from PDF title page (SPIE eBooks Website, viewed 2019-06-26).
650 0 $aIntelligent transportation systems.
650 0 $aMultisensor data fusion.
650 0 $aMotor vehicles$xAutomatic control.
650 0 $aTraffic congestion.
650 0 $aAlgorithms.
650 0 $aComputer algorithms.
650 6 $aSystèmes de transport intelligents.
650 6 $aFusion multicapteurs.
650 6 $aVéhicules automobiles$xCommande automatique.
650 6 $aEmbouteillages (Circulation)
650 6 $aAlgorithmes.
650 7 $aalgorithms.$2aat
650 7 $aAlgorithms.$2fast$0(OCoLC)fst00805020
650 7 $aIntelligent transportation systems.$2fast$0(OCoLC)fst01723430
650 7 $aMotor vehicles$xAutomatic control.$2fast$0(OCoLC)fst01027725
650 7 $aMultisensor data fusion.$2fast$0(OCoLC)fst01029095
650 7 $aTraffic congestion.$2fast$0(OCoLC)fst01154060
655 0 $aElectronic books.
655 4 $aElectronic books.
710 2 $aSociety of Photo-optical Instrumentation Engineers,$epublisher.
776 08 $iPrint version:$z1510627642$z9781510627642$w(DLC) 2019001144
830 0 $aSPIE Press monograph ;$vPM305.
856 40 $uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio16626992.001$zACADEMIC - Electronics & Semiconductors
856 40 $uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio16626992.002$zACADEMIC - Transportation Engineering
852 8 $blweb$hEBOOKS