Optimum Signal Processing
This revised edition is an unabridged and corrected republication of thesecond edition of this book published by McGraw-Hill Publishing Company,New York, NY, in 1988 (ISBN 0-07-047794-9), and also published earlierby Macmillan, Inc., New York, NY, 1988 (ISBN 0-02-389380-X). Allcopyrights to this work reverted to Sophocles J. Orfanidis in 1996.The content of the 2007 republication remains the same as that of the1988 edition, except for some corrections, the deletion from theAppendix of the Fortran and C function listings, which are now availableonline, and the addition of MATLAB versions of all the functions. A pdfversion of the book, as well as all the computer programs, can bedownloaded freely from the web page:http://www.ece.rutgers.edu/~orfanidi/osp2e
Why Read This Book
You should read Optimum Signal Processing because it delivers a rigorous, mathematically clear treatment of estimation, detection, spectral analysis, and filtering while also providing practical MATLAB code and worked algorithms you can run and adapt. You will gain both theory and hands-on tools for designing DSP solutions across audio/speech, radar, and communications applications.
Who Will Benefit
Graduate students, practicing signal-processing engineers, and researchers with some background in signals and probability who need a unified, code-enabled reference for statistical DSP, filter design, spectral methods, and applied algorithms for audio, radar, and communications.
Level: Advanced — Prerequisites: Undergraduate calculus, linear algebra, signals & systems, and probability theory; familiarity with basic programming (MATLAB or C/Fortran helps to run the book's examples).
Key Takeaways
- Derive and apply optimum linear estimators and detectors (Wiener, matched filters, ML, MAP).
- Design and analyze FIR and IIR digital filters using frequency-domain and minimax methods.
- Implement FFT-based spectral analysis and parametric spectral estimation (AR/LP methods).
- Develop and evaluate adaptive filtering algorithms including LMS and RLS for tracking nonstationary signals.
- Apply statistical signal-processing tools to practical problems in audio/speech, radar, and communications.
- Use the provided MATLAB implementations to prototype, test, and reproduce the book’s algorithms.
Topics Covered
- Introduction and Mathematical Preliminaries
- Signals, Systems, and Frequency-Domain Representations
- Linear Estimation: Least Squares and Wiener Filters
- Statistical Foundations: Random Processes and Power Spectra
- Detection Theory and Signal Detection in Noise
- Maximum Likelihood, Bayesian Estimation, and Cramér-Rao Bounds
- Digital Filter Design: FIR Techniques and Windowing
- IIR Filter Design and Realization
- Fast Fourier Transform and Classical Spectral Analysis
- Parametric Spectral Estimation and Linear Prediction (AR Models)
- Adaptive Filtering: LMS, RLS and Applications
- Wavelets and Multiresolution Signal Analysis
- Applications: Audio/Speech Processing, Communications, and Radar
- Appendices: MATLAB Functions and Computational Notes (code available online)
Languages, Platforms & Tools
How It Compares
Shares rigorous statistical treatment with S. M. Kay's Fundamentals of Statistical Signal Processing but is broader in practical DSP topics and includes extensive MATLAB code; compared to Oppenheim & Schafer it is more focused on optimal estimation/detection and statistical methods rather than introductory discrete-time signal processing.
