Estimation, control, and the discrete Kalman filter
著者
書誌事項
Estimation, control, and the discrete Kalman filter
(Applied mathematical sciences, v. 71)
Springer-Verlag, c1989
- : New York
- : Berlin
大学図書館所蔵 全79件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
Bibliography: p. [264]-265
Includes index
内容説明・目次
内容説明
In 1960, R. E. Kalman published his celebrated paper on recursive min imum variance estimation in dynamical systems [14]. This paper, which introduced an algorithm that has since been known as the discrete Kalman filter, produced a virtual revolution in the field of systems engineering. Today, Kalman filters are used in such diverse areas as navigation, guid ance, oil drilling, water and air quality, and geodetic surveys. In addition, Kalman's work led to a multitude of books and papers on minimum vari ance estimation in dynamical systems, including one by Kalman and Bucy on continuous time systems [15]. Most of this work was done outside of the mathematics and statistics communities and, in the spirit of true academic parochialism, was, with a few notable exceptions, ignored by them. This text is my effort toward closing that chasm. For mathematics students, the Kalman filtering theorem is a beautiful illustration of functional analysis in action; Hilbert spaces being used to solve an extremely important problem in applied mathematics. For statistics students, the Kalman filter is a vivid example of Bayesian statistics in action. The present text grew out of a series of graduate courses given by me in the past decade. Most of these courses were given at the University of Mas sachusetts at Amherst.
目次
1 Basic Probability.- 1.1. Definitions.- 1.2. Probability Distributions and Densities.- 1.3. Expected Value, Covariance.- 1.4. Independence.- 1.5. The Radon-Nikodym Theorem.- 1.6. Continuously Distributed Random Vectors.- 1.7. The Matrix Inversion Lemma.- 1.8. The Multivariate Normal Distribution.- 1.9. Conditional Expectation.- 1.10. Exercises.- 2 Minimum Variance Estimation-How the Theory Fits.- 2.1. Theory Versus Practice-Some General Observations.- 2.2. The Genesis of Minimum Variance Estimation.- 2.3. The Minimum Variance Estimation Problem.- 2.4. Calculating the Minimum Variance Estimator.- 2.5. Exercises.- 3 The Maximum Entropy Principle.- 3.1. Introduction.- 3.2. The Notion of Entropy.- 3.3. The Maximum Entropy Principle.- 3.4. The Prior Covariance Problem.- 3.5. Minimum Variance Estimation with Prior Covariance.- 3.6. Some Criticisms and Conclusions.- 3.7. Exercises.- 4 Adjoints, Projections, Pseudoinverses.- 4.1. Adjoints.- 4.2. Projections.- 4.3. Pseudoinverses.- 4.4. Calculating the Pseudoinverse in Finite Dimensions.- 4.5. The Grammian.- 4.6. Exercises.- 5 Linear Minimum Variance Estimation.- 5.1. Reformulation.- 5.2. Linear Minimum Variance Estimation.- 5.3. Unbiased Estimators, Affine Estimators.- 5.4. Exercises.- 6 Recursive Linear Estimation (Bayesian Estimation).- 6.1. Introduction.- 6.2. The Recursive Linear Estimator.- 6.3. Exercises.- 7 The Discrete Kalman Filter.- 7.1. Discrete Linear Dynamical Systems.- 7.2. The Kalman Filter.- 7.3. Initialization, Fisher Estimation.- 7.4. Fisher Estimation with Singular Measurement Noise.- 7.5. Exercises.- 8 The Linear Quadratic Tracking Problem.- 8.1. Control of Deterministic Systems.- 8.2. Stochastic Control with Perfect Observations.- 8.3. Stochastic Control with Imperfect Measurement.- 8.4. Exercises.- 9 Fixed Interval Smoothing.- 9.1. Introduction.- 9.2. The Rauch, Tung, Streibel Smoother.- 9.3. The Two-Filter Form of the Smoother.- 9.4. Exercises.- Appendix A Construction Measures.- Appendix B Two Examples from Measure Theory.- Appendix C Measurable Functions.- Appendix D Integration.- Appendix E Introduction to Hilbert Space.- Appendix F The Uniform Boundedness Principle and Invertibility of Operators.
「Nielsen BookData」 より