Description |
1 online resource. |
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text txt rdacontent |
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computer c rdamedia |
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online resource cr rdacarrier |
Series |
Synthesis lectures on signal processing ; #12. 1932-1236
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Reproduction |
Electronic reproduction. Perth, W.A. Available via World Wide Web. |
Note |
Online resource; title from PDF title page (Morgan & Claypool, viewed on October 16, 2013). |
Bibliography |
Includes bibliographical references (pages 67-70). |
Contents |
1. Introduction. |
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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. |
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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. |
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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. |
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5. Conclusion -- Notation -- Bibliography -- Authors' biographies. |
Indexed In: |
Compendex |
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INSPEC |
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Google scholar |
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Google book search |
Subject |
MATLAB.
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Kalman filtering.
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dynamical system |
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parameter estimation |
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tracking |
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state space model |
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sequential |
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Bayesian estimation |
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linearity |
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Gaussian noise |
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Kalman filter |
Added Author |
Banavar, Mahesh K., author.
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Spanias, Andreas, author.
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Ebooks Corporation
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Related To |
Print version: 9781627051392 |
ISBN |
9781627051408 (electronic bk.) |
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1627051406 (electronic bk.) |
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1627051392 |
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9781627051392 |
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9781627051392 (pbk.) |
UPC # |
10.2200/S00534ED1V01Y201309SPR012 doi |
OCLC # |
EBC1495851 |
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