Steven Kay

Fundamentals of Statistical Signal Processing

Detection Theory, Volume 2

• Gebundenes Buch

Produktbeschreibung

This book focuses on techniques for obtaining optimal detection algorithms for implementation on digital computers.KEY TOPICS:The book explains statistical and signal processing in the context of numerous practical examples, focusing on current detection applications - especially problems in speech and communications. The book makes extensive use of MATLAB, and program listings are included wherever appropriate. Topics covered include: probability density functions and properties; statistical decision theory for both deterministic and random signals; signals with unknown parameters; white and colored Gaussian noise; non-Gaussian noise; detectors; model change detection; complex extensions; vector generalization and array processing. This is the perfect companion to Fundamentals of Statistical Signal Processing, Vol. 1: Estimation Theory.MARKET:For practicing electrical engineers building detectors for real-world applications. Also for electronics students and researchers.

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Produktdetails

• Verlag: Pearson Education
• Seitenzahl: 576
• Erscheinungstermin: 27. Januar 1998
• Englisch
• Abmessung: 246mm x 185mm x 38mm
• Gewicht: 1038g
• ISBN-13: 9780135041352
• ISBN-10: 013504135X
• Artikelnr.: 22317508

Autorenporträt

STEVEN M. KAY is Professor of Electrical Engineering at the University of Rhode Island and a leading expert in signal processing.

Inhaltsangabe

(NOTE: Most chapters begin with an Introduction and Summary.)
1. Introduction.
Detection Theory in Signal Processing. The Detection Problem. The
Mathematical Detection Problem. Hierarchy of Detection Problems. Role of
Asymptotics. Some Notes to the Reader.
2. 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.
3. 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.
4. Deterministic Signals.
Matched Filters. Generalized Matched Filters. Multiple Signals. Linear
Model. Signal Processing Examples. Reduced Form of the Linear Model1.
5. Random Signals.
Estimator-Correlator. Linear Model1. Estimator-Correlator for Large Data
Records. General Gaussian Detection. Signal Processing Example. Detection
Performance of the Estimator-Correlator.
6. 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.
7. 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.
8. 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.
9. 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.
10. 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.
11. Summary of Detectors.
Detection Approaches. Linear Model. Choosing a Detector. Other Approaches
and Other Texts.
12. 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.
13. Complex/Vector Extensions, and Array Processing.
Known PDFs. PDFs with Unknown Parameters. Detectors for Vector
Observations. Estimator-Correlator for Large Data Records. Signal
Processing Examples. PDF of GLRT for Complex Linear Model. Review of
Important Concepts. Random Processes and Time Series Modeling.