Statistical Signal Processing in Engineering
A problem-solving approach to statistical signal processing for practicing engineers, technicians, and graduate students
This book takes a pragmatic approach in solving a set of common problems engineers and technicians encounter when processing signals. In writing it, the author drew on his vast theoretical and practical experience in the field to provide a quick-solution manual for technicians and engineers, offering field-tested solutions to most problems engineers can encounter. At the same time, the book delineates the basic concepts and applied mathematics underlying each solution so that readers can go deeper into the theory to gain a better idea of the solution’s limitations and potential pitfalls, and thus tailor the best solution for the specific engineering application.
Uniquely, Statistical Signal Processing in Engineering can also function as a textbook for engineering graduates and post-graduates. Dr. Spagnolini, who has had a quarter of a century of experience teaching graduate-level courses in digital and statistical signal processing methods, provides a detailed axiomatic presentation of the conceptual and mathematical foundations of statistical signal processing that will challenge students’ analytical skills and motivate them to develop new applications on their own, or better understand the motivation underlining the existing solutions.
Throughout the book, some real-world examples demonstrate how powerful a tool statistical signal processing is in practice across a wide range of applications.
- Takes an interdisciplinary approach, integrating basic concepts and tools for statistical signal processing
- Informed by its author’s vast experience as both a practitioner and teacher
- Offers a hands-on approach to solving problems in statistical signal processing
- Covers a broad range of applications, including communication systems, machine learning, wavefield and array processing, remote sensing, image filtering and distributed computations
- Features numerous real-world examples from a wide range of applications showing the mathematical concepts involved in practice
- Includes MATLAB code of many of the experiments in the book
Statistical Signal Processing in Engineering is an indispensable working resource for electrical engineers, especially those working in the information and communication technology (ICT) industry. It is also an ideal text for engineering students at large, applied mathematics post-graduates and advanced undergraduates in electrical engineering, applied statistics, and pure mathematics, studying statistical signal processing.
Why Read This Book
You will get a pragmatic, problem-solving guide that translates statistical signal processing theory into field-tested recipes you can apply to real engineering problems. The book balances concise mathematical explanations with practical examples across audio/speech, radar, communications and DSP algorithms so you can both implement solutions quickly and understand their limitations.
Who Will Benefit
Practicing engineers, technicians, and graduate students with some signal-processing background who need ready-to-use statistical methods for audio, radar, and communications problems.
Level: Intermediate — Prerequisites: Undergraduate calculus and linear algebra, basic probability and random processes, introductory signal processing (discrete-time signals, filtering, Fourier transforms), and familiarity with MATLAB or Python for numerical work.
Key Takeaways
- Apply estimation and detection techniques to real-world signal-processing problems, including performance trade-offs and practical approximations
- Design and implement digital filters and use FFT-based spectral analysis for stationary and nonstationary signals
- Develop and tune adaptive filtering algorithms (e.g., LMS, RLS) for noise cancellation, echo suppression, and tracking
- Use wavelet and time–frequency methods to analyze transient or nonstationary signals common in audio and radar
- Model and process communication and radar signals with statistical tools for detection, parameter estimation, and interference mitigation
- Evaluate algorithm performance using statistical bounds, Monte Carlo simulations, and robustness checks to guide real implementations
Topics Covered
- 1. Introduction and Problem-Solving Philosophy
- 2. Probability, Random Variables and Vectors — Practical Tools
- 3. Random Processes and Spectral Representations
- 4. Estimation Theory for Engineers (LS, ML, MAP, Cramér–Rao)
- 5. Detection Theory and Hypothesis Testing
- 6. Digital Filter Design and Implementation
- 7. FFTs, Spectral Analysis and Practical Spectral Estimators
- 8. Wavelets and Time–Frequency Analysis
- 9. Adaptive Filtering: Algorithms and Applications
- 10. Statistical Methods in Communications Systems
- 11. Radar Signal Processing: Detection, Tracking, and Clutter
- 12. Audio and Speech Processing Applications
- 13. Implementation Considerations, Numerical Issues and Tools
- Appendices: Mathematical Background, Tables and Code Snippets
Languages, Platforms & Tools
How It Compares
Compared with Kay's Fundamentals of Statistical Signal Processing (more rigorous theory) and Haykin's Adaptive Filter Theory (deep focus on adaptive algorithms), Spagnolini emphasizes pragmatic, application-driven solutions and field-tested recipes bridging theory and practice.
