| This book provides an overview and introduction to signal detection and estimation. The book contains numerous examples solved in detail. Since some material on signal detection could be very complex and require a lot of background in engineering math, a chapter and various sections to cover such background are included, so that one can easily understand the intended material. Probability theory and stochastic processes are prerequisites to the fundamentals of signal detection and parameter estimation. Consequently, Chapters 1, 2, and 3 carefully cover these topics. Chapter 2 covers the different distributions that may arise in radar and communication systems. The chapter is presented in such a way that one may not need to use other references.
In a one-semester graduate course on “Signal Detection and Estimation,” the material to cover should be:
Chapter 5 Statistical Decision Theory
Chapter 6 Parameter Estimation
Chapter 8 Representation of Signals
Chapter 9 The General Gaussian Problem
Chapter 10 Detection and Parameter Estimation
and perhaps part of Chapter 7 on filtering. The book can also be used in a twosemester course on “Signal Detection and Estimation” covering in this case: Chapters 5 to 8 for the first semester and then Chapters 9 to 12 for the second
semester.
Many graduate courses on the above concepts are given in two separate courses; one on probability theory and random processes, and one on signal detection and estimation. In this case, for the first graduate course on “Probability Theory, Random Variables, and Stochastic Processes,” one may cover:
Chapter 1 Probability Concepts
Chapter 2 Distributions
Chapter 3 Random Processes
Chapter 4 Discrete-Time Random Process
This book is primarily designed for the study of statistical signal detection and parameter estimation. Such concepts require a good knowledge of the fundamental notions on probability, random variables, and stochastic processes. In Chapter 1, we present concepts on probability and random variables. In Chapter 2, we discuss some important distributions that arise in many engineering applications such as radar and communication systems. Probability theory is a prerequisite for Chapters 3 and 4, in which we cover stochastic processes and some applications. Similarly, the fundamentals of stochastic processes will be essential for proper understanding of the subsequent topics, which cover the fundamentals of signal detection and parameter estimation. Some applications of adaptive thresholding radar constant false alarm rate (CFAR) detection will be presented in Chapter 11. In Chapter 12, we consider the concepts of adaptive CFAR detection using multiple sensors and data fusion. This concept of adaptive thresholding CFAR detection will also be introduced in spread spectrum communication systems. |
