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An introduction to Kalman filtering with MATLAB examples /
The Kalman filter is the Bayesian optimum solution to the problem of sequentially estimating the states of a dynamical system in which the state evolution and measurement processes are both linear and Gaussian. Given the ubiquity of such systems, the Kalman filter finds use in a variety of applicati...
| Main Authors: | , , |
|---|---|
| Format: | eBook |
| Language: | English |
| Published: |
San Rafael, California (1537 Fourth Street, San Rafael, CA 94901 USA) :
Morgan & Claypool,
2014.
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| Series: | Synthesis digital library of engineering and computer science.
Synthesis lectures on signal processing ; # 12. |
| Subjects: | |
| Online Access: | Abstract with links to full text |
| LEADER | 04418nam a2200733 i 4500 | ||
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| 001 | 201309SPR012 | ||
| 005 | 20160320103534.0 | ||
| 006 | m eo d | ||
| 007 | cr cn |||m|||a | ||
| 008 | 131016s2014 caua foab 000 0 eng d | ||
| 020 | |a 9781627051408 |q (electronic bk.) | ||
| 020 | |z 9781627051392 |q (pbk.) | ||
| 024 | 7 | |a 10.2200/S00534ED1V01Y201309SPR012 |2 doi | |
| 035 | |a (CaBNVSL)swl00402797 | ||
| 035 | |a (OCoLC)860909544 | ||
| 040 | |a CaBNVSL |b eng |e rda |c CaBNVSL |d CaBNVSL | ||
| 050 | 4 | |a QA402.3 |b .K685 2014 | |
| 082 | 0 | 4 | |a 629.8312 |2 23 |
| 100 | 1 | |a Kovvali, Narayan V. S. K., |e author. | |
| 245 | 1 | 3 | |a An introduction to Kalman filtering with MATLAB examples / |c Narayan Kovvali, Mahesh Banavar, and Andreas Spanias. |
| 264 | 1 | |a San Rafael, California (1537 Fourth Street, San Rafael, CA 94901 USA) : |b Morgan & Claypool, |c 2014. | |
| 300 | |a 1 PDF (ix, 71 pages) : |b illustrations. | ||
| 336 | |a text |2 rdacontent | ||
| 337 | |a electronic |2 isbdmedia | ||
| 338 | |a online resource |2 rdacarrier | ||
| 490 | 1 | |a Synthesis lectures on signal processing, |x 1932-1694 ; |v # 12 | |
| 500 | |a Part of: Synthesis digital library of engineering and computer science. | ||
| 500 | |a Series from website. | ||
| 504 | |a Includes bibliographical references (pages 67-70). | ||
| 505 | 0 | |a 1. Introduction -- | |
| 505 | 8 | |a 2. The estimation problem -- 2.1 Background -- 2.1.1 Example: maximum-likelihood estimation in Gaussian noise -- 2.2 Linear estimation -- 2.3 The Bayesian approach to parameter estimation -- 2.3.1 Example: estimating the bias of a coin -- 2.4 Sequential Bayesian estimation -- 2.4.1 Example: the 1-D Kalman filter -- | |
| 505 | 8 | |a 3. The Kalman filter -- 3.1 Theory -- 3.2 Implementation -- 3.2.1 Sample MATLAB code -- 3.2.2 Computational issues -- 3.3 Examples -- 3.3.1 Target tracking with radar -- 3.3.2 Channel estimation in communications systems -- 3.3.3 Recursive least squares (RLS) adaptive filtering -- | |
| 505 | 8 | |a 4. Extended and decentralized Kalman filtering -- 4.1 Extended Kalman filter -- 4.1.1 Example: predator-prey system -- 4.2 Decentralized Kalman filtering -- 4.2.1 Example: distributed object tracking -- | |
| 505 | 8 | |a 5. Conclusion -- Notation -- Bibliography -- Authors' biographies. | |
| 506 | |a Abstract freely available; full-text restricted to subscribers or individual document purchasers. | ||
| 510 | 0 | |a Compendex | |
| 510 | 0 | |a Google book search | |
| 510 | 0 | |a Google scholar | |
| 510 | 0 | |a INSPEC | |
| 520 | 3 | |a The Kalman filter is the Bayesian optimum solution to the problem of sequentially estimating the states of a dynamical system in which the state evolution and measurement processes are both linear and Gaussian. Given the ubiquity of such systems, the Kalman filter finds use in a variety of applications, e.g., target tracking, guidance and navigation, and communications systems. The purpose of this book is to present a brief introduction to Kalman filtering. The theoretical framework of the Kalman filter is first presented, followed by examples showing its use in practical applications. Extensions of the method to nonlinear problems and distributed applications are discussed. A software implementation of the algorithm in the MATLAB programming language is provided, as well as MATLAB code for several example applications discussed in the manuscript. | |
| 530 | |a Also available in print. | ||
| 538 | |a Mode of access: World Wide Web. | ||
| 538 | |a System requirements: Adobe Acrobat Reader. | ||
| 588 | |a Title from PDF title page (viewed on October 16, 2013). | ||
| 630 | 0 | 0 | |a MATLAB. |
| 650 | 0 | |a Kalman filtering. | |
| 653 | |a Bayesian estimation | ||
| 653 | |a dynamical system | ||
| 653 | |a Gaussian noise | ||
| 653 | |a Kalman filter | ||
| 653 | |a linearity | ||
| 653 | |a parameter estimation | ||
| 653 | |a sequential | ||
| 653 | |a state space model | ||
| 653 | |a tracking | ||
| 700 | 1 | |a Banavar, Mahesh K., |e author. | |
| 700 | 1 | |a Spanias, Andreas., |e author. | |
| 776 | 0 | 8 | |i Print version: |z 9781627051392 |
| 830 | 0 | |a Synthesis digital library of engineering and computer science. | |
| 830 | 0 | |a Synthesis lectures on signal processing ; |v # 12. |x 1932-1694 | |
| 856 | 4 | 8 | |3 Abstract with links to full text |u http://dx.doi.org/10.2200/S00534ED1V01Y201309SPR012 |
| 942 | |c EB | ||
| 999 | |c 81081 |d 81081 | ||
| 952 | |0 0 |1 0 |4 0 |7 0 |9 73101 |a MGUL |b MGUL |d 2016-03-20 |l 0 |r 2016-03-20 |w 2016-03-20 |y EB | ||