[PDF] Applied State Estimation and Association by Chaw-Bing Chang, Keh-Ping Dunn

Applied State Estimation and Association by Chaw-Bing Chang, Keh-Ping Dunn

Ebook download pdf file Applied State Estimation and Association 9780262034005 by Chaw-Bing Chang, Keh-Ping Dunn English version

Download Applied State Estimation and Association PDF

  • Applied State Estimation and Association
  • Chaw-Bing Chang, Keh-Ping Dunn
  • Page: 432
  • Format: pdf, ePub, mobi, fb2
  • ISBN: 9780262034005
  • Publisher: MIT Press

Download Applied State Estimation and Association




Ebook download pdf file Applied State Estimation and Association 9780262034005 by Chaw-Bing Chang, Keh-Ping Dunn English version

Overview

Applied state estimation and association is an important area for practicing engineers in aerospace, electronics, and defense industries, used in such tasks as signal processing, tracking, and navigation. 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. The MIT Press

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