000 | 04463nam a2200745 i 4500 | ||
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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. |
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020 |
_z9781627051392 _qpbk. |
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024 | 7 |
_a10.2200/S00534ED1V01Y201309SPR012 _2doi |
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035 | _a(CaBNVSL)swl00402797 | ||
035 | _a(OCoLC)860909544 | ||
040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
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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. |
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300 |
_a1 PDF (ix, 71 pages) : _billustrations. |
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336 |
_atext _2rdacontent |
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337 |
_aelectronic _2isbdmedia |
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338 |
_aonline resource _2rdacarrier |
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490 | 1 |
_aSynthesis lectures on signal processing, _x1932-1694 ; _v# 12 |
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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. |
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700 | 1 |
_aSpanias, Andreas., _eauthor. |
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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 |