Detection of Signals in Noise (Electrical Science Ser.)
The Second Edition is an updated revision to the authors highly successful and widely used introduction to the principles and application of the statistical theory of signal detection. This book emphasizes those theories that have been found to be particularly useful in practice including principles applied to detection problems encountered in digital communications, radar, and sonar.
Why Read This Book
You will learn the foundational statistical principles and practical methods for deciding whether a signal is present in noisy data, with worked emphasis on problems that arise in radar, sonar, and digital communications. Whalen distills theory into concrete detector designs (matched filters, correlators, likelihood-ratio tests) and performance measures you can apply to real systems.
Who Will Benefit
Engineers and researchers working on radar/sonar/communications receiver design or anyone needing a rigorous, application-focused introduction to statistical detection theory.
Level: Advanced — Prerequisites: Undergraduate calculus and linear algebra, probability and random processes (basic stochastic processes), and familiarity with linear systems and basic signal processing concepts.
Key Takeaways
- Derive and apply likelihood-ratio and Neyman–Pearson tests to practical detection problems
- Design and understand matched filters, correlators, and optimum linear detectors in Gaussian noise
- Analyze detector performance with ROC curves, false alarm and detection probabilities, and SNR tradeoffs
- Handle coherent and noncoherent detection schemes used in communications, radar, and sonar
- Model and treat multiple-hypothesis and multi-channel detection problems commonly encountered in practical systems
Topics Covered
- Introduction and historical perspective on detection problems
- Mathematical and statistical preliminaries
- Detection criteria: Bayes, Neyman–Pearson, and minimax approaches
- Likelihood ratio tests and their properties
- Detection in Gaussian noise: matched filters and correlators
- Performance measures: probability of detection, false alarm, and ROC analysis
- Noncoherent and envelope detection methods
- Multiple hypothesis and multi-channel detection
- Applications to digital communications: symbol detection and demodulation
- Applications to radar and sonar: pulse detection, integration, and practical considerations
- Extensions and practical issues: imperfect models, colored noise, and mismatched filters
- Appendices: mathematical tools and commonly used integrals
Languages, Platforms & Tools
How It Compares
A classic concise complement to Van Trees' Detection, Estimation, and Modulation Theory and to Kay's Fundamentals of Statistical Signal Processing (Detection Theory); Whalen is more application-focused and historically foundational, while Van Trees is broader and Kay provides a more modern, exercise-rich treatment.
