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. Review of Systems, Signals. Hypothesis Testing. Non-Parametric and Sequential Detection. Detection of Signals in White Gaussian Noise. Detection of Known Signals in Colored Gaussian Noise: Continuous Time Case. Detection of Known Signals in Colored Gaussian Noise: Matrix Based. Detection of Unknown Signals: Discrete Time Case. Wavelets. Polyspectra Time Frequency Representations and Cyclostationary Spectral Density. Estimation. Spectral Estimation. Appendices.