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迭代估計理論基礎與應用(英文版)/中法卓越工程師培養工程

  • 作者：李顥|責編:張瀟
  • 出版社：上海交大
  • ISBN：9787313265241
  • 出版日期：2022/10/01
  • 裝幀：平裝
  • 頁數：139
人民幣：RMB 58 元      售價：元
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內容大鋼
    本書為「中法卓越工程師培養工程」系列教材之一。全書共6章，主要內容為迭代估計理論和經典建模，包括狀態、系統模型、觀測模型、貝葉斯推理、卡爾曼濾波、串序蒙特卡羅方法以及迭代估計理論的應用等，每章都配有算例供讀者參閱和練習，方便讀者學習和理解相關知識。
    本書可作為具有一定英語和電腦基礎的理工科學生的控制理論課程教學用書，也，可供相關教學人員閱讀參考。
作者介紹
李顥|責編:張瀟
目錄
Preface
Chapter 1 Introduction
  1.1  State
  1.2  System model
  1.3  Measurement(or observation)model
  1.4  Recursive estimation
Chapter 2 Basic Spi~t And Utilities
  2.1  Mathematical notations
  2.2  Kalman filter
    2.2.1  Linear—Gaussian modelling
    2.2.2  Prediction—update formalism
  2.3  Data fusion perspective
    2.3.1  Optimal weighted average
    2.3.2  Derivation of the update formalism of the Kalman filter
  2.4  Application
    2.4.1  Application description
    2.4.2  One—dimensional simplification of vehicle localization
    2.4.3  Two—dimensional vehicle lOCalization
  2.5  Summary
Chapter 3 System Models
  3.1  Estimation for non—deterministic systems：Object tracking
    3.1.1  System modelling for object tracking
    3.1.2  Application of the Kalman filter using the CP，CV，and CA models
    3.1.3  Simulation
    3.1.4  Ideal object tracking
  3.2  System model mismatch
    3.2.1  Under—modelling mismatch：Low—order system model VS.high—order objec
    motion pattern
    3.2.2  Over—modelling mismatch：High—order system model VS.tow-order object
    motion pattern
  3.3  Interacting multiple model(IMM)：Handling non.deterministic system modelling
    3.3.1  Spirit ofmultiple hypotheses merging
    3.3.2  Mathematical notations
    3.3.3  Interacting multiple model f IMM)algorithm and analysis
    3.3.4  Application：IMM object tracking
  3.4  Summary
Chapter 4 Handling Nonlinearity of System And Measurement MOdels
  4.1  Extended Kalman filter(EKF)
  4.2  Extended Kalman filter application
    4.2.1  Application description
    4.2.2  Model local linearization
    4.2.3  Simulation
  4.3  Unscented Kalman filter fUKFl
    4.3.1  Kalman filter in different formalism
    4.3.2  Unscented~ansformafion
    4.3.3  Why「unscented」
    4.3.4  Summary of the unscented Kalman filter
  4.4  Unscented Kalman filter application
    4.4.1  Application description
    4.4.2  Simulation

  4.5  Summary
Chapter 5  Sampling Based Recursive EstimatiOn
  5.1  Bayesian inference for recursive estimation
  5.2  Sequential Monte Carlo(SMC)method
    5.2.1  Sampling：Monte Carlo(MC)method
    5.2.2  Importance sampling(IS)
    5.2.3  Sequential sampling(SS)
    5.2.4  Sequential importance sampling(SIS)
  5.3  Resampling(R)and sequential importance sampling with resampling(SIS／R)
    5.3.1  Resampling
    5.3.2  Sequential importance sampling with resampling(SIS／R)
    5.3.3  Particle filter(PF)
  5.4  Particle filter application
    5.4.1  Application description
    5.4.2  Simulation
  5.5  Handling multimodal ambiguity
  5.6  Summary
Chapter 6 Handling Data Correlation
  6.1  Harm due to naive handling of data correlation
    6.1.1  Estimate consistency
    6.1.2  Circular reasoning
  6.2  Handling known data correlation
  6.3  Handling unknown data correlation
  6.4  Split covariance intersection filter(Split CIF)
    6.4.1  Handling both known independence and unknown correlation
    6.4.2  Estimates in split form and basic formalism
    6.4.3  Formalism for partial observation
    6.4.4  Efficient and accurate implementation
    6.4.5  Proof for the convexity of the Woptimization
  6.5  Split covariance intersection filter application
    6.5.1  Application description
    6.5.2  Simulation
  6.6  Summary
Bibliography

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