Adaptive Filtering: Prediction and Control
Ideal for advanced undergraduate and graduate classes, this treatment consists of two parts. The first section concerns deterministic systems, covering models, parameter estimation, and adaptive prediction and control. The second part examines stochastic systems, exploring optimal filtering and prediction, parameter estimation, adaptive filtering and prediction, and adaptive control. Extensive appendices offer a summary of relevant background material, making this volume largely self-contained. Readers will find that these theories, formulas, and applications are related to a variety of fields, including biotechnology, aerospace engineering, computer sciences, and electrical engineering.
Why Read This Book
You should read this book if you want a rigorous, unified foundation linking adaptive filtering, prediction and control for discrete‑time linear systems — it shows how estimation and control problems are two sides of the same coin. You will learn theory that remains relevant to modern DSP, communications, radar and control applications and get practical guidance on algorithmic implementation and extensions to stochastic and nonlinear settings.
Who Will Benefit
Advanced undergraduates, graduate students, and practicing engineers in DSP, control, communications, or radar who need a deep theoretical and practical grounding in adaptive estimation and adaptive control.
Level: Advanced — Prerequisites: Undergraduate-level linear systems and signals, calculus and linear algebra, basic probability and stochastic processes, and familiarity with state‑space methods and z‑transform techniques.
Key Takeaways
- Formulate adaptive estimation and prediction problems for linear discrete‑time systems and relate them to control objectives.
- Derive and analyze classical adaptive algorithms (parameter estimation, LMS/RLS family in their theoretical context) and their convergence properties.
- Design adaptive prediction schemes and use them as building blocks for adaptive control of deterministic and stochastic systems.
- Analyze optimal filtering and prediction (Wiener/Kalman perspectives) and connect these to practical adaptive implementations.
- Extend linear adaptive methods to address modeling errors and discuss pathways to nonlinear and computer‑implemented adaptive control.
Topics Covered
- 1. Introduction and Scope: Adaptive Filtering, Prediction and Control
- 2. Models for Discrete‑Time Linear Systems
- 3. Deterministic Parameter Estimation and Identification
- 4. Adaptive Prediction for Deterministic Systems
- 5. Adaptive Control in Deterministic Settings
- 6. Random Processes and Stochastic System Models
- 7. Optimal Filtering and Prediction (Wiener and Kalman Perspectives)
- 8. Stochastic Parameter Estimation and Adaptive Filtering
- 9. Adaptive Prediction and Adaptive Control for Stochastic Systems
- 10. Extensions to Nonlinear Systems and Practical Considerations
- 11. Numerical Issues, Implementation on Digital Computers
- 12. Case Studies and Applications (communications, radar, audio/speech)
- Appendices: Mathematical Background and Proofs
Languages, Platforms & Tools
How It Compares
Covers similar theoretical ground to Simon Haykin's Adaptive Filter Theory and Widrow & Stearns' Adaptive Signal Processing, but places stronger emphasis on prediction/control unity and a control‑oriented viewpoint; more theory‑heavy and less tutorial/algorithm‑centric than later texts such as Ali H. Sayed's Fundamentals of Adaptive Filtering.
