Digital Filters: Analysis, Design and Applications
This final year/postgraduate text for courses in digital filters or digital signal processing deals with the construction of algorithms that filter data into useful information. It starts with the basics and goes on to cover advanced topics such as recursive and non-recursive filters (including optimization techniques), wave digital filters and DFTs. A new chapter on the application of digital signal processing offers up-to-date techniques and there are new problems and examples throughout. A solutions manual is available (0-07-002122-8). Other features new to this second edition include chapters on quasi-Newton and minimax optimization algorithms for the design of recursive filters and equalizers, and efficient and robust algorithms for the design of nonrecursive filters and differentiators. HLP computer language is now replaced with Pascal.
Why Read This Book
You should read this book if you need a deep, practical treatment of digital filter theory and design: you will learn both classical and modern algorithmic techniques for FIR and IIR filters, optimization-based design methods (quasi-Newton, minimax), and how DFT/FFT and wave-digital concepts tie into real applications. It balances theory and worked examples so you can move from understanding to implementing robust filters in practice.
Who Will Benefit
Advanced undergraduates, graduate students, and practicing DSP engineers who design or implement digital filters and need rigorous methods for analysis, optimization, and realization.
Level: Advanced — Prerequisites: Undergraduate signals & systems (Z-transform, Fourier), linear algebra, basic complex analysis; familiarity with DSP terminology and comfort reading mathematical derivations; MATLAB or similar helpful for exercises.
Key Takeaways
- Design robust FIR filters using windowing, frequency-sampling and optimization techniques (including Parks–McClellan-style approaches).
- Design efficient IIR/recursive filters using classical analog-to-digital transformations and modern optimization (least-squares, quasi-Newton, minimax).
- Analyze finite-wordlength and numerical issues and select stable, efficient realization structures for implementation.
- Apply DFT/FFT theory to filter design and spectral analysis and understand practical algorithmic trade-offs.
- Understand wave-digital filter concepts and how they can be used to model and implement stable digital networks.
Topics Covered
- Introduction and Review of Signals, Systems and Transforms
- Z-Transform and Sampling Theory Essentials
- Basic Filter Structures and Realization Methods
- Nonrecursive (FIR) Filter Design: Windows and Frequency Sampling
- Optimal FIR Design: Chebyshev (Parks–McClellan) and Least-Squares Methods
- Recursive (IIR) Filter Design: Analog Prototypes and Bilinear/Impulse Methods
- Optimization Algorithms for Recursive Filters: Quasi-Newton and Minimax
- Finite-Wordlength Effects and Numerical Robustness
- Wave Digital Filters and Network-Based Approaches
- DFT, FFT and Applications to Filtering and Spectral Analysis
- Efficient Algorithms and Implementation Considerations
- Applications of Digital Filtering (examples and case studies)
Languages, Platforms & Tools
How It Compares
More focused on filter design and optimization than Oppenheim & Schafer's Discrete-Time Signal Processing (which is broader); complements classic Parks & Burrus/Parks–McClellan treatments by adding recursive-filter optimization and wave-digital perspectives.
