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Fundamentals of Statistical Signal Processing Detection Theory

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ISBN-10: 013504135X

ISBN-13: 9780135041352

Edition: 1998

Authors: Steven M. Kay

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Book details

Copyright year: 1998
Publisher: Prentice Hall PTR
Publication date: 1/27/1998
Binding: Hardcover
Pages: 576
Size: 7.50" wide x 9.75" long x 1.00" tall
Weight: 2.486

Detection Theory in Signal Processing
The Detection Problem
The Mathematical Detection Problem
Hierarchy of Detection Problems
Role of Asymptotics
Some Notes to the Reader
Summary of Important PDFs
Fundamental Probability Density Functionshfil Penalty - M and Properties
Quadratic Forms of Gaussian Random Variables
Asymptotic Gaussian PDF
Monte Carlo Performance Evaluation
Number of Required Monte Carlo Trials
Normal Probability Paper
MATLAB Program to Compute Gaussian Right-Tail Probability and its Inverse
MATLAB Program to Compute Central and Noncentral c 2 Right-Tail Probability
MATLAB Program for Monte Carlo Computer Simulation
Statistical Decision Theory I
Neyman-Pearson Theorem
Receiver Operating Characteristics
Irrelevant Data
Minimum Probability of Error
Bayes Risk
Multiple Hypothesis Testing
Neyman-Pearson Theorem
Minimum Bayes Risk Detector - Binary Hypothesis
Minimum Bayes Risk Detector - Multiple Hypotheses
Deterministic Signals
Matched Filters
Generalized Matched Filters
Multiple Signals
Linear Model
Signal Processing Examples
Reduced Form of the Linear Model1
Random Signals
Linear Model1
Estimator-Correlator for Large Data Records
General Gaussian Detection
Signal Processing Example
Detection Performance of the Estimator-Correlator
Statistical Decision Theory II
Composite Hypothesis Testing
Composite Hypothesis Testing Approaches
Performance of GLRT for Large Data Records
Equivalent Large Data Records Tests
Locally Most Powerful Detectors
Multiple Hypothesis Testing
Asymptotically Equivalent Tests - No Nuisance Parameters
Asymptotically Equivalent Tests - Nuisance Parameters
Asymptotic PDF of GLRT
Asymptotic Detection Performance of LMP Test
Alternate Derivation of Locally Most Powerful Test
Derivation of Generalized ML Rule
Deterministic Signals with Unknown Parameters
Signal Modeling and Detection Performance
Unknown Amplitude
Unknown Arrival Time
Sinusoidal Detection
Classical Linear Model
Signal Processing Examples
Asymptotic Performance of the Energy Detector
Derivation of GLRT for Classical Linear Model
Random Signals with Unknown Parameters
Incompletely Known Signal Covariance
Large Data Record Approximations
Weak Signal Detection
Signal Processing Example
Derivation of PDF for Periodic Gaussian Random Process
Unknown Noise Parameters
General Considerations
White Gaussian Noise
Colored WSS Gaussian Noise
Signal Processing Example
Derivation of GLRT for Classical Linear Model for s 2 Unknown
Rao Test for General Linear Model with Unknown Noise Parameters
Asymptotically Equivalent Rao Test for Signal Processing Example
NonGaussian Noise
NonGaussian Noise Characteristics
Known Deterministic Signals
Deterministic Signals with Unknown Parameters
Signal Processing Example
Asymptotic Performance of NP Detector for Weak Signals
BRao Test for Linear Model Signal with IID NonGaussian Noise
Summary of Detectors
Detection Approaches
Linear Model
Choosing a Detector
Other Approaches and Other Texts
Model Change Detection
Description of Problem
Extensions to the Basic Problem
Multiple Change Times
Signal Processing Examples
General Dynamic Programming Approach to Segmentation
MATLAB Program for Dynamic Programming
Complex/Vector Extensions, and Array Processing
Known PDFs
PDFs with Unknown Parameters
Detectors for Vector Observations
Estimator-Correlator for Large Data Records