Robust Estimation and Failure Detection

1 Introduction.- 2 Estimation and Failure Detection: An Overview.- 2.1 Introduction.- 2.2 Ever Since Wiener.- 2.3 Failure Detection and Isolation.- 2.4 Summary.- 3 Discrete-Time Robust Estimation.- 3.1 Introduction.- 3.2 Plants with an Uncertain Noise Model.- 3.3 Plants with Uncertain Dynamics and Noise Model.- 3.4 Extension to Steady State.- 3.5 Robust Fixed-Interval Smoothing.- 3.6 Numerical Examples.- 3.7 Related Work.- 4 Stochastic Interpretation of Robust Estimation: Risk Sensitivity.- 4.1 Introduction.- 4.2 The Risk Sensitive Optimal Estimation Problem.- 4.3 Extension to Systems with Modeling Uncertainty.- 4.4 Numerical Comparison of Error Density Functions.- 4.5 Summary.- 5 Robust Failure Detection and Isolation.- 5.1 Introduction.- 5.2 Problem Description.- 5.3 A Likelihood Ratio Test with Robustness Properties.- 5.4 Likelihood Ratio Tests and Plant Uncertainties.- 5.5 FDI with Robustness to Failure Mode, Noise and Plant Uncertainties.- 5.6 Summary.- 6 Two Applications.- 6.1 Introduction.- 6.2 Application to an Underwater Vehicle.- 6.3 Application to Reentry Vehicle attitude Control Systems.- A The Kalman Filter.- A.1 Problem Description.- A.2 The One-Step Predictor.- A.3 Measurement Update and the Filtered Estimate.- A.4 Gaussian Disturbance.- A.5 The Innovation Process.- A.6 Linear Time-Invariant Systems.- A.7 The Wiener Filter.- A.8 Smoothing.- A.8.1 Fixed-Interval Smoothing.- A.8.2 Fixed-Point and Fixed-Lag Smoothing.- A.9 The Extended Kaiman Filter (EKF).- A.10 Summary of Equations and Additional Remarks.- B Outputs of Linear Systems and Their Quadratic Forms.- B.1 Moments of Linear Systems Outputs.- B.2 Probability Density Functions of Gaussian Quadratic Forms.- C Continuous-Time Robust Estimation.- C.1 Introduction.- C.2 Problem Formulation.- C.3 Derivation of the Estimator.- C.4 Related Work.- D Application Data.- D.1 Underwater Vehicle Application.- D.1.1 The Plant.- D.1.2 Description of Robust Filter Design.- D.2 Reentry Vehicle Application.- D.2.1

From the reviews:
"The book provides a novel solution of a class of robust estimation problems and includes convincing application studies. ... The material presented in the book is accessible to the novice in both filtering and detection theory. ... The chapters of the book are very well- structured, presented with clarity, and the reader's understanding is helped with some examples. ... I strongly recommend it to researchers and practitioners in the field of estimation and failure detection, who will certainly find this book an important reference." (Theodor- Dan Popescu, Studies in Informatics and Control, Vol. 8 (2), 1999)