Principles of Signal Detection and Parameter Estimation

ISBN-10: 0387765425
ISBN-13: 9780387765426
Edition: 2008
Authors: Bernard C. Levy
List price: $99.00 Buy it from $92.08
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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  More...

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Book details

List price: $99.00
Copyright year: 2008
Publisher: Springer
Publication date: 7/7/2008
Binding: Hardcover
Pages: 664
Size: 6.25" wide x 9.50" long x 1.50" tall
Weight: 2.244
Language: English

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 detection of Gaussian signals in noise, the expectation maximization algorithm, and the detection of Markov chain signals. Numerous examples and detailed derivations along with homework problems following each chapter are included.

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

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