Statisical and Adaptive Signal Processi (Artech House Signal Processing Library)
Signal processing is an essential topic for all practicing and aspiring electrical engineers to understand no matter what specific area they are involved in. Originally published by McGraw-Hill* and now reissued by Artech House, this definitive volume offers a unified, comprehensive and practical treatment of statistical and adaptive signal processing. Written by leading experts in industry and academia, the book covers the most important aspects of the subject, such as spectral estimation, signal modeling, adaptive filtering, and array processing. This unique resource provides balanced coverage of implementation issues, applications, and theory, making it a smart choice for professional engineers and students alike. The book presents clear examples, problem sets, and computer experiments that help readers master the material and learn how to implement various methods presented in the chapters. This invaluable reference also includes a set of Matlab[registered] functions that engineers can use to solve real-world problems in the field. The book is packed with over 3,000 equations and more than 300 illustrations.
Why Read This Book
You should read this book if you want a unified, mathematically sound reference that connects spectral estimation, parametric signal modeling, adaptive filters, and array (beamforming/DOA) methods with practical implementation notes and experiments. It balances theory and application so you can both understand algorithm derivations and implement them for real-world signal-processing tasks.
Who Will Benefit
Graduate students, DSP engineers, and researchers working on spectral analysis, adaptive filtering, or array processing who need a single, in-depth reference to theory and practical algorithms.
Level: Advanced — Prerequisites: Linear algebra (eigenvalues/eigenvectors), probability and random processes, signals and systems, and basic digital signal processing (DFT, basic filtering). Familiarity with MATLAB is helpful for the book's exercises.
Key Takeaways
- Implement and compare nonparametric and parametric spectral estimation methods (periodogram, Welch, AR, MUSIC, ESPRIT).
- Formulate, estimate, and validate statistical signal models (AR/MA/ARMA) and choose model order using criteria like AIC/MDL.
- Derive and implement adaptive filtering algorithms (LMS, NLMS, RLS) and understand their convergence, robustness, and computational trade-offs.
- Design and analyze array processing and beamforming techniques, including DOA estimation using subspace methods.
- Apply covariance estimation and whitening techniques and evaluate performance under finite-sample and noise conditions.
- Translate theoretical algorithms into practical MATLAB experiments and diagnostics for real-data scenarios.
Topics Covered
- Introduction and Statistical Foundations (random processes, estimation basics)
- Nonparametric Spectral Estimation (periodogram, smoothing, Welch, multitaper overview)
- Parametric Spectral Estimation and Signal Modeling (AR/MA/ARMA models, Yule-Walker, Burg)
- Model Order Selection and Parameter Estimation (AIC, MDL, PCA-based considerations)
- Linear Prediction and Applications
- Adaptive Filtering Fundamentals (LMS family, normalized LMS)
- Recursive Algorithms and Fast Adaptive Methods (RLS, fast implementations)
- Performance Analysis and Convergence of Adaptive Algorithms
- Practical Implementation Issues and Computer Experiments (finite-sample effects, numerical stability)
- Array Signal Processing and Beamforming (conventional beamformers, MVDR/Capon)
- Subspace Methods for DOA Estimation (MUSIC, ESPRIT) and Array Calibration
- Applications: Communications, Radar, Speech/Audio Examples
- Appendices: Mathematical tools, MATLAB exercises, reference tables
Languages, Platforms & Tools
How It Compares
Covers broader spectral-estimation and array-processing ground than Haykin's Adaptive Filter Theory (which focuses more narrowly on adaptive filters); complements Stoica & Moses's spectral-analysis texts by providing more implementation experiments and adaptive/array content.
