This text provides an introduction to the field of stochastic dynamic systems, their estimation and control. Including the provision of computer derivations of key results, the book covers both state-space methods and methods based on the polynomial approach, with similarities and differences between the two highlighted. Some nonlinear aspects (including the bispectrum and extended Kalman filter) are also introduced and analyzed. Processes with complex-valued data are treated wherever convenient as this is of interest in many signal processing and communication problems.
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The aim of this text is to give a comprehensive introduction to the field of stochastic dynamic systems, their estimation and control, including the provision of complete derivations of key results. The book covers both state-space methods and methods based on the polynomial approach, with similarities and differences between the two highlighted. Some nonlinear aspects (including the bispectrum and extended Kalman filter) are also introduced and analyzed. Processes with complex-valued data are treated wherever convenient as this is of interest in many signal and communication problems.
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First Sentence:
A "stochastic system" is understood here as a dynamic system that has some kind of uncertainty. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
predictor identity, polynomial formalism, cautious controller, optimal state estimate, uncorrelated white noise sequences, complete state information, optimal regulator, optimal state estimation, smoothing estimate, spectral factorization, predictor gain, controller poles, weighting pattern, stochastic dynamic systems, zeros inside the unit circle, prediction error variance, covariance elements, multiple model approach, error spectrum, estimation error variance, covariance function, certainty equivalence principle, causal part, output innovations, optimal control law Key Phrases - Capitalized Phrases (CAPs): (learn more)
New York, Prentice Hall, Academic Press, John Wiley, Exercises Exercise, Monte Carlo, Englewood Cliffs, Introduction Some, Linear Estimation, Upper Saddle River, Remark Note, Linear Quadratic Gaussian Design, Minimum Variance Filters, Proof Apparently, Proof Equation New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
A "stochastic system" is understood here as a dynamic system that has some kind of uncertainty. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
predictor identity, polynomial formalism, cautious controller, optimal state estimate, uncorrelated white noise sequences, complete state information, optimal regulator, optimal state estimation, smoothing estimate, spectral factorization, predictor gain, controller poles, weighting pattern, stochastic dynamic systems, zeros inside the unit circle, prediction error variance, covariance elements, multiple model approach, error spectrum, estimation error variance, covariance function, certainty equivalence principle, causal part, output innovations, optimal control law Key Phrases - Capitalized Phrases (CAPs): (learn more)
New York, Prentice Hall, Academic Press, John Wiley, Exercises Exercise, Monte Carlo, Englewood Cliffs, Introduction Some, Linear Estimation, Upper Saddle River, Remark Note, Linear Quadratic Gaussian Design, Minimum Variance Filters, Proof Apparently, Proof Equation New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 40
books:
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- Optimal Control and Stochastic Estimation: Theory and Applications by Michael J. Grimble on 4 pages
- Optimal Filtering (Dover Books on Electrical Engineering) by Brian D. O. Anderson on 4 pages
- Linear Estimation by Thomas Kailath on page 6, page 135, and page 182
- Stochastic Models, Estimation and Control Volume 3 (Mathematics in Science and Engineering) by Peter S. Maybeck on page 6, page 183, and Back Matter
- Stochastic Processes and Filtering Theory (Mathematics in Science and Engineering) by Andrew H. Jazwinski on page 6, page 122, and page 294
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