000 04463nam a2200745 i 4500
001 6812936
003 IEEE
005 20200413152911.0
006 m eo d
007 cr cn |||m|||a
008 131016s2014 caua foab 000 0 eng d
020 _a9781627051408
_qelectronic bk.
020 _z9781627051392
_qpbk.
024 7 _a10.2200/S00534ED1V01Y201309SPR012
_2doi
035 _a(CaBNVSL)swl00402797
035 _a(OCoLC)860909544
040 _aCaBNVSL
_beng
_erda
_cCaBNVSL
_dCaBNVSL
050 4 _aQA402.3
_b.K685 2014
082 0 4 _a629.8312
_223
090 _a
_bMoCl
_e201309SPR012
100 1 _aKovvali, Narayan V. S. K.,
_eauthor.
245 1 3 _aAn introduction to Kalman filtering with MATLAB examples /
_cNarayan Kovvali, Mahesh Banavar, and Andreas Spanias.
264 1 _aSan Rafael, California (1537 Fourth Street, San Rafael, CA 94901 USA) :
_bMorgan & Claypool,
_c2014.
300 _a1 PDF (ix, 71 pages) :
_billustrations.
336 _atext
_2rdacontent
337 _aelectronic
_2isbdmedia
338 _aonline resource
_2rdacarrier
490 1 _aSynthesis lectures on signal processing,
_x1932-1694 ;
_v# 12
538 _aMode of access: World Wide Web.
538 _aSystem requirements: Adobe Acrobat Reader.
500 _aPart of: Synthesis digital library of engineering and computer science.
500 _aSeries from website.
504 _aIncludes bibliographical references (pages 67-70).
505 0 _a1. Introduction --
505 8 _a2. 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 _a3. 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 _a4. 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 _a5. Conclusion -- Notation -- Bibliography -- Authors' biographies.
506 1 _aAbstract freely available; full-text restricted to subscribers or individual document purchasers.
510 0 _aCompendex
510 0 _aINSPEC
510 0 _aGoogle scholar
510 0 _aGoogle book search
520 3 _aThe 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 _aAlso available in print.
588 _aTitle from PDF title page (viewed on October 16, 2013).
630 0 0 _aMATLAB.
650 0 _aKalman filtering.
653 _adynamical system
653 _aparameter estimation
653 _atracking
653 _astate space model
653 _asequential
653 _aBayesian estimation
653 _alinearity
653 _aGaussian noise
653 _aKalman filter
700 1 _aBanavar, Mahesh K.,
_eauthor.
700 1 _aSpanias, Andreas.,
_eauthor.
776 0 8 _iPrint version:
_z9781627051392
830 0 _aSynthesis digital library of engineering and computer science.
830 0 _aSynthesis lectures on signal processing ;
_v# 12.
_x1932-1694
856 4 2 _3Abstract with links to resource
_uhttp://ieeexplore.ieee.org/servlet/opac?bknumber=6812936
856 4 0 _3Abstract with links to full text
_uhttp://dx.doi.org/10.2200/S00534ED1V01Y201309SPR012
999 _c562024
_d562024