This newly revised edition of a classic Artech House book provides you with a comprehensive and current understanding of signal detection and estimation. Featuring a wealth of new and expanded material, the second edition introduces the concepts of adaptive CFAR detection and distributed CA-CFAR detection. The book provides complete explanations of the mathematics you need to fully master the material, including probability theory, distributions, and random processes. Containing numerous solved examples, the book helps you apply the material to projects in the field involving signal processing, radar, and communications. Packed with over 2,100 equations, 230 figures and 183 problems, this authoritative resource covers a wide range of critical topics, from parameter estimation and filtering, to representation of signals and Gaussian processes. The problems presented at the end of each chapter make this book particularly well suited for self-study and for use as a text for graduate-level electrical engineering courses.
Probability Concepts - Sets and Probability. Random Variables. Moments. Two and Higher Dimensional Random Variables. Transformation of Random Variables.; Distributions - Discrete Random Variables: Bernoulli, Binomial, Multinomial, Geometric, Pascal, Hypergeometric, and Poisson. Continuous Random Variables: Uniform, Normal, Exponential, Laplace, Gamma, Beta, Chi-square, Rayleigh, Rice, Maxwell, Nakagami-m, Student's t, F, and Cauchy. Some Special Distributions: Bivariate and Multivariate Gaussian, Weibull, Log-normal, K, and Generalized Compound.; Random Processes - Expectations. Properties of Correlation Functions. Some Random Processes. Power Spectral Density. Linear Time Invariant Systems. Ergodicity. Sampling Theorem. Continuity, Differentiation and Integration. Hilbbert Transform and Analytic Signals. Thermal Noise; Discrete-Time Random Processes - Matrix and Linear Algebra. Definitions. AR, MA, ARMA Random Processes. Markov Chains. Continuous Markov Chains.; Statistical Decision Theory - Bayes Criterion. Minimax Criterion. Neyman-Pearson Criterion. Composite Hypothesis Testing. Sequential Detection.; Parameter Estimation - Maximum Likelihood Estimation.Generalized Likelihood Ratio Test. Some Criteria for Good Estimators. Bayes Estimation. Cramer-Rao Inequality. Multiple Parameter Estimation. Best Linear Unbiased Estimator. Least Square Estimation. Recursive Least Square Estimation.; Filtering - Linear Transformation and Orthogonality Principle. Wiener Filters. Discrete Wiener Filters. Kalman Filter.; Representation of Signals - Othogonal Functions. Linear Differential Operators and Integral Equations. Representation of Random Processess.; The General Gaussian Problem - Binary Detection. Same Covariance. Same Mean. Same Mean and Symmetric Hypothesis.; Detection and Parameter Estimation - Binary Detection. M-ary Detection. Linear Estimation. Nonlinear Estimation. General Binary Detection with Unwanted Parameters. Binary Detection in Colored Noise.; Adaptive Thresholding CFAR Detection - Radar Elementary Concepts. Principles of Adaptive CFAR Detection. Adaptive Thresholding in Code Acquisition of Direct Sequence Spread Spectrum Signals.; Distributed CFAR Detection - Distributed CA-CFAR Detection. Further Results.;
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Mourad Barkat
Mourad Barkat is a professor in the department of electronics at the University of Constantine, Algeria. He is a well-published author and a senior member of the IEEE. He is a member of Tau Beta Pi and Eta Kappa Nu. Dr. Barkat received his B. S. with high honors: Magna Cum Laude, M. S., and Ph. D. in electrical engineering from Syracuse University.