Detection Estimation and Modulation Theory, Part I

Since 1968 and after 30 printings of the first edition, Part I of DEMT has been the textbook for the two generations of students and researchers that have designed the signal processing in many of our operational systems. The Second Edition includes subsequent advances, retains clarity of explanation, and promises to be the text and reference for future generations. - Dr. Arthur B. Baggeroer, Ford Professor Emeritus, MIT The First Edition of Detection, Estimation, and Modulation Theory, Part I, enjoyed a long useful life. However, in the forty-four years since its publication, there have been a large number of changes: 1. The basic detection and estimation theory has remained the same but numerous new results and algorithms have been obtained. 2. The exponential growth in computational capability has enabled us to implement algorithms that were only of theoretical interest in 1968. 3. The theoretical results from DEMT have been widely applied in operational systems. 4. Simulation became more widely used in system design and analysis, research, and teaching. The Second Edition is a significant expansion of the first edition with 450 pages of new material. Chapter 2 in the First Edition, Classical Detection and Estimation Theory, is expanded into four chapters. Many more examples are developed in detail to enhance readability, and more non-Gaussian models are included. A large number of significant developments that are appropriate for an introductory text-including global Bayesian bounds, efficient computational algorithms, equivalent estimation algorithms, sequential estimation, and importance sampling-are added. The Fisher and Bayesian linear Gaussian models are studied in more detail. The First Edition emphasized continuous-time random processes. The Second Edition includes a comprehensive development of linear estimation of discrete-time random processes leading to discrete-time Wiener and Kalman filters. A brief introduction to Bayesian estimation of non-Gaussian processes is included. An expanded version of material from Part III develops optimum detectors for continuous-time and discrete-time random processes that can be implemented using Wiener or Kalman filters. As imperative today as it has been since its original publication in 1968, this work is sure to remain the leading reference for engineers who need to apply detection and estimation theory in diverse systems.