Includes bibliographical references and index.

"Provides a model-based "bridge" for signal processors/control engineers enabling a coupling and motivation for model development and subsequent processor designs/applications - Incorporates an in-depth treatment of the subspace approach that applies a variety of the subspace algorithm to synthesized examples and actual applications - Introduces new, fast subspace identifiers, capable of developing the required model for processing/controls Market description: Primary audience: advanced seniors, 1st year graduate student (engineering, sciences) Secondary audience: engineering professionals"-- Provided by publisher.

Online resource; title from digital title page (viewed on April 01, 2019).

Cover; Title Page; Copyright; Contents; Preface; Acknowledgements; Glossary; Chapter 1 Introduction; 1.1 Background; 1.2 Signal Estimation; 1.3 Model-Based Processing; 1.4 Model-Based Identification; 1.5 Subspace Identification; 1.6 Notation and Terminology; 1.7 Summary; MATLAB Notes; References; Problems; Chapter 2 Random Signals and Systems; 2.1 Introduction; 2.2 Discrete Random Signals; 2.3 Spectral Representation of Random Signals; 2.4 Discrete Systems with Random Inputs; 2.4.1 Spectral Theorems; 2.4.2 ARMAX Modeling; 2.5 Spectral Estimation

2.5.1 Classical (Nonparametric) Spectral Estimation2.5.1.1 Correlation Method (Blackman-Tukey); 2.5.1.2 Average Periodogram Method (Welch); 2.5.2 Modern (Parametric) Spectral Estimation; 2.5.2.1 Autoregressive (All-Pole) Spectral Estimation; 2.5.2.2 Autoregressive Moving Average Spectral Estimation; 2.5.2.3 Minimum Variance Distortionless Response (MVDR) Spectral Estimation; 2.5.2.4 Multiple Signal Classification (MUSIC) Spectral Estimation; 2.6 Case Study: Spectral Estimation of Bandpass Sinusoids; 2.7 Summary; Matlab Notes; References; Problems

Chapter 3 State-Space Models for Identification3.1 Introduction; 3.2 Continuous-Time State-Space Models; 3.3 Sampled-Data State-Space Models; 3.4 Discrete-Time State-Space Models; 3.4.1 Linear Discrete Time-Invariant Systems; 3.4.2 Discrete Systems Theory; 3.4.3 Equivalent Linear Systems; 3.4.4 Stable Linear Systems; 3.5 Gauss-Markov State-Space Models; 3.5.1 Discrete-Time Gauss-Markov Models; 3.6 Innovations Model; 3.7 State-Space Model Structures; 3.7.1 Time-Series Models; 3.7.2 State-Space and Time-Series Equivalence Models; 3.8 Nonlinear (Approximate) Gauss-Markov State-Space Models

3.9 SummaryMATLAB Notes; References; Chapter 4 Model-Based Processors; 4.1 Introduction; 4.2 Linear Model-Based Processor: Kalman Filter; 4.2.1 Innovations Approach; 4.2.2 Bayesian Approach; 4.2.3 Innovations Sequence; 4.2.4 Practical Linear Kalman Filter Design: Performance Analysis; 4.2.5 Steady-State Kalman Filter; 4.2.6 Kalman Filter/Wiener Filter Equivalence; 4.3 Nonlinear State-Space Model-Based Processors; 4.3.1 Nonlinear Model-Based Processor: Linearized Kalman Filter; 4.3.2 Nonlinear Model-Based Processor: Extended Kalman Filter

4.3.3 Nonlinear Model-Based Processor: Iterated-Extended Kalman Filter4.3.4 Nonlinear Model-Based Processor: Unscented Kalman Filter; 4.3.5 Practical Nonlinear Model-Based Processor Design: Performance Analysis; 4.3.6 Nonlinear Model-Based Processor: Particle Filter; 4.3.7 Practical Bayesian Model-Based Design: Performance Analysis; 4.4 Case Study: 2D-Tracking Problem; 4.5 Summary; MATLAB Notes; References; Problems; Chapter 5 Parametrically Adaptive Processors; 5.1 Introduction; 5.2 Parametrically Adaptive Processors: Bayesian Approach