Signal Detection and Estimation (Artech House Radar Library (Hardcover))
This reference spells out the fundamentals of Augmented with 1024 equations, 138 references and 82 figures and 69 problems, this book provides an introduction to and overview of signal detection and estimation. detection and estimation theory, reviews mathemat ical techniques and gives the essential background needed to understand the more advanced material, provides detailed examples stated and solved showing all the necessary steps, and contains chapter-end problems and provides step-by-step solutions that facilitate self-study. Each chapter provides an introduction, summary, problems and list of references and expands upon material covered in the previous chapter.
Why Read This Book
You should read this book if you want a compact, worked-example approach to classical detection and estimation theory: it walks you through Neyman-Pearson tests, likelihood methods, CRLB, and practical detector design with step-by-step solutions. The many solved examples and end-of-chapter problems make it especially useful for self-study and for translating theory into hands-on calculations.
Who Will Benefit
Graduate students and practicing engineers in communications, radar, or DSP who need a practical, worked-through introduction to statistical detection and estimation theory.
Level: Intermediate — Prerequisites: Probability and random processes (basic stochastic calculus not required), linear algebra, signals and systems fundamentals, and familiarity with Gaussian noise models; MATLAB familiarity is helpful but not required.
Key Takeaways
- Derive and apply the Neyman-Pearson likelihood ratio test for binary and simple composite hypotheses.
- Compute and use the Cramér-Rao lower bound and Fisher information for parameter estimation problems.
- Formulate and solve maximum likelihood and Bayesian estimators for common signal models.
- Design matched filters and detectors for deterministic and stochastic signals in Gaussian noise.
- Analyze detector performance using ROC curves and probability-of-error computations.
Topics Covered
- Introduction and overview of detection/estimation problems
- Mathematical preliminaries: probability, random variables, and stochastic processes
- Binary hypothesis testing and the Neyman-Pearson lemma
- Likelihood ratio tests, composite hypotheses, and the GLRT
- Detection of deterministic signals in noise (matched filter, correlation receivers)
- Detection of random/stochastic signals and spectral considerations
- Receiver Operating Characteristic (ROC) analysis and performance measures
- Point estimation: unbiased, biased, and method of moments
- Maximum likelihood and Bayesian estimation techniques
- Cramér-Rao bound and efficiency; Fisher information
- Linear estimation and Wiener filtering
- Recursive estimation and practical examples (basic Kalman concepts / recursive estimators)
- Worked examples, problems and solutions; references and appendices
Languages, Platforms & Tools
How It Compares
More compact and example-oriented than Van Trees' multi-volume treatment and somewhat less comprehensive than Kay's Fundamentals of Statistical Signal Processing; Barkat is stronger on worked examples and step-by-step solutions.
