Fundamentals of Statistical Signal Processing: Detection Theory, Volume 2, 1st edition

Published by Pearson (January 27, 1998) © 1998

  • Steven M. Kay University of Rhode Island
$109.60

  • A print text (hardcover or paperback) 
  • Free shipping
  • Also available for purchase as an ebook from all major ebook resellers, including InformIT.com
(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.

Need help? Get in touch

Video
Play
Privacy and cookies
By watching, you agree Pearson can share your viewership data for marketing and analytics for one year, revocable by deleting your cookies.

Pearson eTextbook: What’s on the inside just might surprise you

They say you can’t judge a book by its cover. It’s the same with your students. Meet each one right where they are with an engaging, interactive, personalized learning experience that goes beyond the textbook to fit any schedule, any budget, and any lifestyle.