Detection of Signals in Noise
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.
Detection processing based upon the fast Fourier transform
Why Read This Book
You will get a concise, practice-oriented presentation of statistical detection theory that connects rigorous likelihood-based methods to real detection problems in radar, sonar, and digital communications. The book emphasizes implementable detectors (including FFT-based processing) and shows how to evaluate performance in realistic noise environments.
Who Will Benefit
Graduate students, DSP/communications engineers, and radar practitioners who need a compact but rigorous reference on detection theory and practical detector design.
Level: Advanced — Prerequisites: Probability and random processes, linear systems and Fourier transforms, basic linear algebra, and familiarity with common DSP concepts (spectra, FFT).
Key Takeaways
- Derive likelihood-ratio tests for deterministic and stochastic signal hypotheses.
- Design and implement matched-filter and correlator receivers for detection in Gaussian noise.
- Apply GLRT and composite-hypothesis procedures when parameters are unknown.
- Use FFT-based spectral processing to build efficient detectors for wideband and spectral-signature signals.
- Evaluate detector performance using ROC curves, detection probability vs. false alarm tradeoffs, and analytic performance formulas.
- Handle practical issues such as colored noise, whitening, multichannel observations, and CFAR approaches.
Topics Covered
- 1. Introduction and Problem Formulation
- 2. Review of Random Processes and Detection Basics
- 3. Neyman-Pearson and Likelihood Ratio Tests
- 4. Detection of Deterministic Signals in Gaussian Noise
- 5. Matched Filters and Correlator Receivers
- 6. Detection in Colored Noise and Whitening Filters
- 7. Detection of Random Signals and Stochastic Models
- 8. Composite Hypotheses and the GLRT
- 9. Multichannel and Multisensor Detection
- 10. FFT-Based Detection and Spectral Methods
- 11. Performance Measures, ROC Analysis, and Approximations
- 12. Practical Considerations: CFAR, Implementation, and Examples
- Appendices (probability results, useful transforms)
Languages, Platforms & Tools
How It Compares
Covers similar practical detection topics as Van Trees' and Kay's detection volumes; more compact and application-focused than Van Trees and slightly more applied than Kay's rigorous, multi-volume treatment.
