Algorithms for Statistical Signal Processing
Keeping pace with the expanding, ever more complex applications of DSP, this authoritative presentation of computational algorithms for statistical signal processing focuses on advanced topics ignored by other books on the subject. Algorithms for Convolution and DFT. Linear Prediction and Optimum Linear Filters. Least-Squares Methods for System Modeling and Filter Design. Adaptive Filters. Recursive Least-Squares Algorithms for Array Signal Processing. QRD-Based Fast Adaptive Filter Algorithms. Power Spectrum Estimation. Signal Analysis with Higher-Order Spectra. For Electrical Engineers, Computer Engineers, Computer Scientists, and Applied Mathematicians.
Why Read This Book
You should read this book if you need a practical, algorithm-level guide to advanced statistical signal processing: it shows efficient, numerically stable implementations of RLS, QRD-based adaptive filters, convolution/DFT algorithms, and spectral-estimation methods. You will gain the computational tools and insights needed to implement and optimize DSP algorithms in research or production systems.
Who Will Benefit
Graduate students, DSP researchers, and senior engineers working on adaptive filtering, array processing, spectral estimation, or implementing high-performance statistical DSP algorithms.
Level: Advanced — Prerequisites: Familiarity with linear systems and signals, probability & stochastic processes, matrix algebra and numerical linear algebra, and basic DSP concepts (DFT/FFT, FIR/IIR filtering).
Key Takeaways
- Implement numerically stable convolution and DFT/FFT-based algorithms for large-scale processing
- Derive and apply linear prediction and optimum linear filtering techniques for estimation problems
- Formulate and solve least-squares system identification and filter-design problems
- Develop and analyze adaptive filters including RLS and QRD-based fast adaptive algorithms
- Estimate power spectra with parametric and nonparametric methods and use higher-order spectra for non-Gaussian/nonlinear analysis
Topics Covered
- Mathematical and Numerical Preliminaries
- Algorithms for Convolution and the Discrete Fourier Transform
- Linear Prediction and Optimum Linear Filters
- Least-Squares Methods for System Modeling and Filter Design
- Adaptive Filtering: Theory and Algorithms
- Recursive Least-Squares Algorithms for Array Signal Processing
- QRD-Based Fast Adaptive Filter Algorithms
- Power Spectrum and Spectral Estimation Methods
- Signal Analysis Using Higher-Order Spectra
- Numerical Stability, Complexity, and Implementation Issues
- Appendices: Matrix Decompositions and Computational Recipes
Languages, Platforms & Tools
How It Compares
More implementation- and algorithm-focused than Haykin's Adaptive Filter Theory (which emphasizes adaptive-filter theory and applications) and complements Stoica & Moses's spectral-analysis texts by providing concrete numerical algorithms and QRD/RLS techniques.
