Detection Theory Applications and Digital Signal Processing [Hardcover]

Using simplified notation and a practical approach, Detection Theory: Applications and Digital Signal Processing introduces the principles of detection theory, the necessary mathematics, and basic signal processing methods along with some recently developed statistical techniques. Throughout the book, the author keeps the needs of practicing engineers firmly in mind. His presentation and choice of topics allows students to quickly become familiar with the detection and signal processing fields and move on to more advanced study and practice. The author also presents many applications and wide-ranging examples that demonstrate how to apply the concepts to real-world problems.

INTRODUCTION
General Philosophy
Detection and Estimation Philosophy
Description of Spaces involved in the Decision
Summary
REVIEW OF DETERMINISTIC AND RANDOM SYSTEM AND SIGNAL CONCEPTS
Some Mathematical and Statistical Background
Systems and Signals (Deterministic and Random)
Transformation of Random Variables
Summary
INTRODUCTION TO SIGNAL PROCESSING
Introduction
Data Structure and Sampling
Discrete-Time Transformations
Filtering
Finite Impulse Response Filter
The Fast Fourier Transform
Fast Correlation
Periodogram (Power Spectral Density Estimate)
Wavelets
Summary
HYPOTHESIS TESTING
Introduction
Bayes Detection
Maximum A Posteriori (MAP) Detection
Maximum Likelihood (ML) Criterion
Minimum Probability of Error Criterion
Min-Max Criterion
Neyman-Pearson Criterion
Multiple Hypothesis Testing
Composite Hypothesis Testing
Receiver Operator Characteristic Curves and Performance
Summary
NON-PARAMETRIC AND SEQUENTIAL LIKELIHOOD RATIO DETECTORS
Introduction
Non-Parametric Detection
Wilcoxon Detector
Sequential Detection
Summary
DETECTION OF SIGNALS IN GAUSSIAN lS.