This monograph contains a number of problems with signal detection theory, presenting a generalized observation model for signal detection problems. The model includes several interesting and common special cases, such as those describing additive noise, multiplicative noise and signal-dependent noise.

The purpose of this book is to introduce the reader to the basic theory of signal detection and estimation. It is assumed that the reader has a working knowledge of applied probabil ity and random processes such as that taught in a typical first-semester graduate engineering course on these subjects. This material is covered, for example, in the book by Wong (1983) in this series. More advanced concepts in these areas are introduced where needed, primarily in Chapters VI and VII, where continuous-time problems are treated. This book is adapted from a one-semester, second-tier graduate course taught at the University of Illinois. However, this material can also be used for a shorter or first-tier course by restricting coverage to Chapters I through V, which for the most part can be read with a background of only the basics of applied probability, including random vectors and conditional expectations. Sufficient background for the latter option is given for exam pIe in the book by Thomas (1986), also in this series.

A Primer of Signal Detection Theory is being reprinted to fill the gap in literature on Signal Detection Theory--a theory that is still important in psychology, hearing, vision, audiology, and related subjects. This book is intended to present the methods of Signal Detection Theory to a person with a basic mathematical background. It assumes knowledge only of elementary algebra and elementary statistics. Symbols and terminology are kept at a basic level so that the eventual and hoped for transfer to a more advanced text will be accomplished as easily as possible. Intended for undergraduate students at an introductory level, the book is divided into two sections. The first part introduces the basic ideas of detection theory and its fundamental measures. Its aim is to enable the reader to be able to understand and compute these measures. It concludes with a detailed analysis of a typical experiment and a discussion of some of the problems which can arise for the potential user of detection theory. The second section considers three more advanced topics: threshold theory, the extension of detection theory, and an examination of Thurstonian scaling procedures.

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.

The book is divided into two main sections, the first comprising Chapters 1 to 5, and the second, Chapters 6 to 8. The first section introduces the basic ideas of detection theory and its fundamental measures. The aim is to enable the reader to be able to understand and compute these measures. The section ends with a detailed working through of a typical experiment and a discussion of some of the problems which can arise for the potential user of detection theory. The second section considers three more advanced topics. The first of these, which is treated thoroughly elsewhere in the literature, is threshold theory. The second topic concerns the extension of detection theory, which customarily requires experiments using more open-ended procedures, such as recall; and the third topic is an examination of Thurstonian scaling procedures which extend signal detection theory in a number of useful ways.