Detection, Estimation, and Modulation Theory, Part III: Radar-Sonar Signal Processing and Gaussian Signals in Noise
* Paperback reprint of one of the most respected classics in the history of engineering publication* Together with the reprint of Part I and the new Part IV, this will be the most complete treatment of the subject available* Provides a highly-readable discussion of Signal Processing and Noise* Features numerous problems and illustrations to help promote understanding of the topics* Contents are highly applicable to current systems
Why Read This Book
You should read this book if you need a rigorous, unified foundation for detection and estimation applied to radar and sonar systems — it develops likelihood-ratio detectors, matched filtering, parameter estimation, and array processing from first principles. Van Trees combines deep theory with many worked problems and examples so you can both understand performance limits and apply those results to real systems.
Who Will Benefit
Graduate students, research engineers, and practicing radar/sonar DSP engineers who design or analyze detectors, estimators, and array processors.
Level: Advanced — Prerequisites: Undergraduate probability & random processes, linear algebra, signals & systems, and familiarity with basic statistical concepts (e.g., PDFs, expectation).
Key Takeaways
- Derive and apply likelihood-ratio tests and Neyman-Pearson theory for detection in Gaussian noise.
- Design and analyze matched filters/correlators and quantify their detection performance (ROC, Pd/Pfa).
- Apply estimation theory (MLE, unbiased estimators) and compute performance bounds such as the Cramér-Rao lower bound for range, Doppler, and bearing estimation.
- Design and evaluate optimum and adaptive array processors (beamforming, direction-of-arrival estimation, sidelobe control).
- Assess multi-target resolution, clutter/interference effects, and practical detector implementations including CFAR concepts.
Topics Covered
- Introduction and signal models for radar and sonar
- Probability, random processes, and Gaussian noise fundamentals
- Detection theory: Neyman-Pearson and likelihood-ratio tests
- Detection of known signals: matched filters and correlators
- Detection with unknown parameters: GLRT and composite hypotheses
- Performance analysis: ROC curves, Pd/Pfa, and operating characteristics
- Parameter estimation: MLE, unbiased estimators, and Cramér-Rao bounds
- Spectral/Doppler processing and correlation methods
- Optimum array processing: beamforming and array gain
- Adaptive array processors and practical beamforming algorithms
- Multitarget detection, resolution, and interference effects
- Practical examples, problems, and implementation considerations
How It Compares
More comprehensive and classical in scope than Richards' 'Fundamentals of Radar Signal Processing' and more theory-oriented and broader than Steven Kay's detection/estimation volumes, which are more tutorial and focused on specific topics.
