This book is my personal statement about the fundamental ideas in statistical signal processing. The book breaks down along four distinct topical lines: mathematical and statistical preliminaries; detection theory; estimation theory; and time series analysis. There is enough material to support a two-semester course in statistical signal processing, but the book may be used for separate one-semester courses in detection theory, estimation theory, or time series analysis. In a detection theory course, Chapters 1 through 5 may be covered in their entirety. In an estimation theory course, Chapter 1 through 3 and 9 through 11 are appropriate. Chapter 9 on least squares is a swing chapter that may be treated as a topic in estimation theory or time series analysis.

Chapter 1 Introduction
Chapter 2 Rudiments of Linear Algebra and Multivariate Normal
Theory
Chapter 3 Sufficiency and MVUB Estimators
Chapter 4 Neyman-Pearson Detectors
Chapter 5 Bayes Detectors
Chapter 6 Maximum Likelihood Estimators
Chapter 7 Bayes Estimators
Chapter 8 Minimum Mean-Squared Error Estimators
Chapter 9 Least Squares
Chapter 10 Linear Prediction
,etc.