Applied state estimation and association / Chaw-Bing Chang and Keh-Ping Dunn.
Material type:
TextSeries: MIT Lincoln laboratory seriesPublication details: Cambridge : MIT Press, ©2016.Description: xxxiv, 438 pages : illustrations (some color) ; 24 cmISBN: - 9780262034005 (hardcover : alk. paper)
- 620.0011 23 C456
| Item type | Current library | Call number | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|
| Books | ISI Library, Kolkata | 620.0011 C456 (Browse shelf(Opens below)) | Available | 138039 |
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| 620 W181 Engineering science | 620 W181 Engineering science | 620.0011 B177 Applied optimization | 620.0011 C456 Applied state estimation and association / | 620.0011 R881 System dynamics : an introduction | 620.001171 C495 Modeling and simulation of system using Matlab and Simulink | 620.001171 C518 Recursive identification and parameter estimation / |
Includes bibliographical references and index.
1. Perimeter estimation --
2. State estimation for linear systems --
3. State estimation for nonlinear systems --
4. Practical considerations in Kalman Filter Design --
5. Multiple model estimation algorithms --
6. Sampling techniques for state estimation --
7. State estimation with multiple sensor systems --
8. Estimation and association with uncertain measurement origin --
9. Multiple hypothesis tracking algorithm --
10. Multiple sensor correlation and fusion with biased measurements --
Appendices.
This book offers a rigorous introduction to both theory and application of state estimation and association. It takes a unified approach to problem formulation and solution development that helps students and junior engineers build a sound theoretical foundation for their work and develop skills and tools for practical applications. Chapters 1 through 6 focus on solving the problem of estimation with a single sensor observing a single object, and cover such topics as parameter estimation, state estimation for linear and nonlinear systems, and multiple model estimation algorithms. Chapters 7 through 10 expand the discussion to consider multiple sensors and multiple objects. The book can be used in a first-year graduate course in control or system engineering or as a reference for professionals. Each chapter ends with problems that will help readers to develop derivation skills that can be applied to new problems and to build computer models that offer a useful set of tools for problem solving. Readers must be familiar with state-variable representation of systems and basic probability theory including random and stochastic processes.
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