Principles of Signal Detection and Parameter Estimation

Name: Principles of Signal Detection and Parameter Estimation
Price: 211.45 USD
Availability: InStock
ISBN: 9780387765426

ISBN-10: 0387765425

ISBN-13: 9780387765426

Edition: 2008

Authors: Bernard C. Levy

List price: $99.99

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Description:

This new textbook is intended for contemporary signal detection and parameter estimation courses offered at advanced undergraduate or graduate levels. It presents a unified treatment of detection problems arising in radar/sonar signal processing and modern digital communication systems. The material is comprehensive in scope and addresses signal processing and communications applications with an emphasis on fundamental principles. In addition to standard topics normally covered in such a course, the author incorporates recent advances in the field, such as the asymptotic performance of detectors, sequential detection, generalized likelihood ratio tests (GLRTs), robust detection, the…

Book details

List price: $99.99
Copyright year: 2008
Publisher: Springer
Publication date: 7/7/2008
Binding: Hardcover
Pages: 664
Size: 6.10" wide x 9.25" long x 1.50" tall
Weight: 2.244
Language: English




Introduction



Book Organization



Complementary Readings


References



Foundations



Binary and M-ary Hypothesis Testing



Introduction



Bayesian Binary Hypothesis Testing



Sufficient Statistics



Receiver Operating Characteristic



Neyman-Pearson Tests



ROC Properties



Minimax Hypothesis Testing



Gaussian Detection



Known Signals in Gaussian Noise



Detection of a Zero-Mean Gaussian Signal in Noise



M-ary Hypothesis Testing



Bayesian M-ary Tests



Sufficient Statistics for M-ary Tests



Performance Analysis



Bounds Based on Pairwise Error Probability



Bibliographical Notes



Problems


References



Tests with Repeated Observations



Introduction



Asymptotic Performance of Likelihood Ratio Tests



Bayesian Sequential Hypothesis Testing



Sequential Probability Ratio Tests



Optimality of SPRTs



Bibliographical Notes



Problems



Proof of Cramer's Theorem


References



Parameter Estimation Theory



Introduction



Bayesian Estimation



Optimum Bayesian Estimator



Properties of the MSE Estimator



Linear Least-squares Estimation



Estimation of Nonrandom Parameters



Bias



Sufficient Statistic



Cramer-Rao Lower Bound



Uniform Minimum Variance Unbiased Estimates



Asymptotic Behavior of ML Estimates



Consistency



Asymptotic Distribution of the ML Estimate



Bibliographical Notes



Problems



Derivation of the RBLS Theorem


References



Composite Hypothesis Testing



Introduction



Uniformly Most Powerful Tests



Invariant Tests



Linear Detection with Interfering Sources



Generalized Likelihood Ratio Tests



Asymptotic Optimality of the GLRT



Multinomial Distributions



Exponential Families



Bibliographical Notes



Problems



Proof of Sanov's Theorem


References



Robust Detection



Introduction



Measures of Model Proximity



Robust Hypothesis Testing



Robust Bayesian and NP Tests



Clipped LR Tests



Asymptotic Robustness



Least Favorable Densities



Robust Asymptotic Test



Robust Signal Detection



Least-Favorable Densities



Receiver Structure



Bibliographical Notes



Problems


References



Gaussian Detection



Karhunen-Loeve Expansion of Gaussian Processes



Introduction



Orthonormal Expansions of Deterministic Signals



Eigenfunction Expansion of Covariance Kernels



Properties of Covariance Kernels



Decomposition of Covariance Matrices/Kernels



Differential Characterization of the Eigenfunctions



Gaussian Reciprocal Processes



Partially Observed Gaussian Reciprocal/Markov Processes



Rational Stationary Gaussian Processes



Karhunen-Loeve Decomposition



Asymptotic Expansion of Stationary Gaussian Processes



Bibliographical Notes



Problems


References



Detection of Known Signals in Gaussian Noise



Introduction



Binary Detection of Known Signals in WGN



Detection of a Single Signal



General Binary Detection Problem



M-ary Detection of Known Signals in WGN



Detection of Known Signals in Colored Gaussian Noise



Singular and Nonsingular CT Detection



Generalized Matched Filter Implementation



Computation of the Distorted Signal g(t)



Noise Whitening Receiver



Bibliographical Notes



Problems


References



Detection of Signals with Unknown Parameters



Introduction



Detection of Signals with Unknown Phase



Signal Space Representation



Bayesian Formulation



GLR Test



Detector Implementation



Detection of DPSK Signals



Detection of Signals with Unknown Amplitude and Phase



Bayesian Formulation



GLR Test



Detection with Arbitrary Unknown Parameters



Waveform Parameter Estimation



Detection of Radar Signals



Equivalent Baseband Detection Problem



Cramer-Rao Bound



ML Estimates and GLR Detector



Ambiguity Function Properties



Bibliographical Notes



Problems


References



Detection of Gaussian Signals in WGN



Introduction



Noncausal Receiver



Receiver Structure



Smoother Implementation



Causal Receiver



Asymptotic Stationary Gaussian Test Performance



Asymptotic Equivalence of Toeplitz and Circulant Matrices



Mean-square Convergence of S[subscript T]



Large Deviations Analysis of the LRT



Detection in WGN



Bibliographical Notes



Problems


References



EM Estimation and Detection of Gaussian Signals with Unknown Parameters



Introduction



Gaussian Signal of Unknown Amplitude in WGN of Unknown Power



EM Parameter Estimation Method



Motonicity Property



Example



Convergence Rate



Large-Sample Covariance Matrix



Parameter Estimation of Hidden Gauss-Markov Models



EM iteration



Double-sweep smoother



Example



GLRT Implementation



Bibliographical Notes



Problems


References



Markov Chain Detection



Detection of Markov Chains with Known Parameters



Introduction



Detection of Completely Observed Markov Chains



Notation and Background



Binary Hypothesis Testing



Asymptotic Performance



Detection of Partially Observed Markov Chains



MAP Sequence Detection



Pointwise MAP Detection



Example: Channel Equalization



Markov Chain Model



Performance Analysis



Bibliographical Notes



Problems


References



Detection of Markov Chains with Unknown Parameters



Introduction



GLR Detector



Model



GLR Test



Per Survivor Processing



Path Extension



Parameter Vector Update



EM Detector



Forward-backward EM



EM Viterbi Detector



Example: Blind Equalization



Convergence Analysis



Convergence Rate



Bibliographical Notes



Problems


References


Index