Adaptive Filtering and Change Detection
Adaptive filtering is a branch of digital signal processing which enables the selective enhancement of desired elements of a signal and the reduction of undesired elements. Change detection is another kind of adaptive filtering for non--stationary signals, and is the basic tool in fault detection and diagnosis. This text takes the unique approach that change detection is a natural extension of adaptive filtering, and the broad coverage encompasses both the mathematical tools needed for adaptive filtering and change detection and the applications of the technology. Real engineering applications covered include aircraft, automotive, communication systems, signal processing and automatic control problems. The unique integration of both theory and practical applications makes this book a valuable resource combining information otherwise only available in separate sources* Comprehensive coverage includes many examples and case studies to illustrate the ideas and show what can be achieved* Uniquely integrates applications to airborne, automotive and communications systems with the essential mathematical tools* Accompanying Matlab toolbox available on the web illustrating the main ideas and enabling the reader to do simulations using all the figures and numerical examples featured This text would prove to be an essential reference for postgraduates and researchers studying digital signal processing as well as practising digital signal processing engineers.
Why Read This Book
You should read this book if you need a unified view of adaptive filtering and change detection — it shows how detection for nonstationary signals is a natural extension of adaptive estimation. You will get both the mathematical foundations (sequential tests, RLS/Kalman methods, GLR/CUSUM) and practical guidance for applying these techniques to fault diagnosis, communications and control problems.
Who Will Benefit
Graduate students, signal processing engineers and researchers working on adaptive filters, sequential detection, fault diagnosis, or time-varying system identification who want a rigorous but application-oriented reference.
Level: Advanced — Prerequisites: Undergraduate probability and statistics, linear algebra, basic DSP and estimation theory (familiarity with least-squares and basic Kalman filtering is helpful).
Key Takeaways
- Formulate adaptive estimation problems using least-squares, RLS and Kalman filtering frameworks.
- Design and implement adaptive filters (LMS, RLS, model-based estimators) for time-varying signals.
- Derive and apply sequential change-detection tests (CUSUM, GLR, likelihood-ratio approaches) and understand their performance tradeoffs.
- Integrate adaptive filtering and change detection for fault detection and diagnosis in engineering systems.
- Evaluate detection performance and tune algorithms for false alarm rate, detection delay, and robustness.
- Apply model-based identification techniques to detect abrupt and gradual changes in dynamical systems.
Topics Covered
- Introduction: Adaptive Filtering and Change Detection — scope and motivating applications
- Signal and System Models for Adaptive Estimation
- Least Squares and Recursive Estimation
- Stochastic State-Space Models and the Kalman Filter
- Classical Adaptive Filters: LMS, NLMS and RLS
- Parameter Tracking and Time-Varying Models
- Foundations of Change Detection: Hypothesis Testing and Likelihood Ratios
- Sequential Detection: CUSUM, GLR and Quickest Detection
- Model-Based Change Detection and Fault Diagnosis
- Practical Issues: Thresholding, Delay, and False Alarms
- Applications: Automotive, Aircraft, Communication Systems and Control
- Implementation Considerations and Examples
- Appendices: Useful Probability, Statistics and Matrix Results
Languages, Platforms & Tools
How It Compares
Compared with Haykin's 'Adaptive Filter Theory' (which focuses on adaptive algorithms and convergence), Gustafsson adds a deep, statistical treatment of sequential change detection and model-based fault diagnosis — complementing Basseville & Nikiforov's focused text on change detection by integrating adaptive estimation methods.
