An Introduction to Statistical Communication Theory: An IEEE Press Classic Reissue - Tapa dura

Using the statistical Bayesian viewpoint, renowned author David Middleton employs statistical decision theory specifically tailored for the general tasks of signal processing. Dr. Middleton also provides a special focus on physical modeling of the canonical channel with real-world examples relating to radar, sonar, and general telecommunications. This book offers a detailed treatment and an array of problems and results spanning an exceptionally broad range of technical subjects in the communications field.

Complete with special functions, integrals, solutions of integral equations, and an extensive, updated bibliography by chapter, An Introduction to Statistical Communication Theory is a seminal reference, particularly for anyone working in the field of communications, as well as in other areas of statistical physics. (Originally published in 1960.)

This IEEE Press Classic Reissue provides an advanced level, yet uniquely fundamental, treatment of the applications of Statistical Communication Theory to a vast spectrum of important physical problems. Included are general treatments of signal detection, estimation, and measurement, and related topics involving information transfer. Using the Bayesian statistical viewpoint, renowned author David Middleton employs statistical decision theory specifically tailored for the general tasks of signal processing. Dr. Middleton also provides a special focus on physical modeling of the canonical channel with real-world examples relating to radar, sonar, and general telecommunications applications. This book offers a detailed treatment and an array of problems and results covering an exceptionally broad range of technical subjects in the communications field, including among others:

• Specific applications of Fourier as well as single- and two-sided LaPlace transform methods
• Evaluation of covariance functions and intensity spectra
• Signal-to-noise ratios in nonlinear systems
• Sampling and interpolation
• Langevin, Fokker-Planck, and Boltzmann equations
• Amplitude, phase, and frequency modulation by noise and signals
• Detection probabilities
• Optimum estimators
• Minimum detectable signals
• Neyman-Pearson and Ideal Observer detection algorithms
• Multiple alternative detection algorithms and performance measures