Sensor and data fusion for intelligent transportation systems
"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 monogr...
Saved in:
| Main Author: | |
|---|---|
| Format: | eBook |
| Language: | English |
| Published: |
Bellingham, Washington, USA :
SPIE Press,
[2019]
|
| Series: | SPIE Press monograph ;
PM305. |
| Subjects: | |
| ISBN: | 1510627650 9781510627659 9781510627642 1510627642 9781510627666 1510627669 9781510627673 1510627677 |
| Physical Description: | 1 online resource (254 pages) |
Cover
Table of contents
| LEADER | 05962cam a2200565Mi 4500 | ||
|---|---|---|---|
| 001 | kn-on1107275079 | ||
| 003 | OCoLC | ||
| 005 | 20240717213016.0 | ||
| 006 | m o d | ||
| 007 | cr cn||||||||| | ||
| 008 | 190626t20192019wau ob 001 0 eng d | ||
| 040 | |a SPIES |b eng |e rda |c SPIES |d OCLCO |d CUS |d UIU |d OCLCF |d UPM |d OCLCQ |d DKU |d UWW |d YDX |d EBLCP |d OCLCO |d OCL |d OCLCQ |d N$T |d OCLCO |d OCLCL | ||
| 020 | |a 1510627650 |q (PDF) | ||
| 020 | |a 9781510627659 |q (electronic bk.) | ||
| 020 | |z 9781510627642 |q (softcover) | ||
| 020 | |z 1510627642 |q (softcover) | ||
| 020 | |z 9781510627666 |q (ePub) | ||
| 020 | |z 1510627669 |q (ePub) | ||
| 020 | |z 9781510627673 |q (Kindle) | ||
| 020 | |z 1510627677 |q (Kindle) | ||
| 024 | 7 | |a 10.1117/3.2525400 |2 doi | |
| 035 | |a (OCoLC)1107275079 |z (OCoLC)1122891281 |z (OCoLC)1231605196 | ||
| 100 | 1 | |a Klein, Lawrence A., |e author. | |
| 245 | 1 | 0 | |a Sensor and data fusion for intelligent transportation systems / |c Lawrence A. Klein. |
| 264 | 1 | |a Bellingham, Washington, USA : |b SPIE Press, |c [2019] | |
| 264 | 4 | |c ©2019 | |
| 300 | |a 1 online resource (254 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a SPIE Press monograph ; |v PM305 | |
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a 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 | |
| 505 | 8 | |a 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. | |
| 506 | |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty | ||
| 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"-- |c Provided by publisher | ||
| 500 | |a Title from PDF title page (SPIE eBooks Website, viewed 2019-06-26). | ||
| 590 | |a Knovel |b Knovel (All titles) | ||
| 650 | 0 | |a Intelligent transportation systems. | |
| 650 | 0 | |a Multisensor data fusion. | |
| 650 | 0 | |a Motor vehicles |x Automatic control. | |
| 650 | 0 | |a Traffic congestion. | |
| 650 | 0 | |a Algorithms. | |
| 650 | 0 | |a Computer algorithms. | |
| 655 | 7 | |a elektronické knihy |7 fd186907 |2 czenas | |
| 655 | 9 | |a electronic books |2 eczenas | |
| 710 | 2 | |a Society of Photo-Optical Instrumentation Engineers, |e publisher. | |
| 776 | 0 | 8 | |i Print version: |z 1510627642 |z 9781510627642 |w (DLC) 2019001144 |
| 830 | 0 | |a SPIE Press monograph ; |v PM305. | |
| 856 | 4 | 0 | |u https://proxy.k.utb.cz/login?url=https://app.knovel.com/hotlink/toc/id:kpSDFITS05/sensor-and-data?kpromoter=marc |y Full text |
Save to List
Cite this
Export Record
Email this