Introduction to nonparametric detection with applications, Volume 119 (Mathematics in Science and Engineering)

✍ Scribed by Gibson (editor)

• Publisher: Academic Press
• Year: 1975
• Tongue: English
• Leaves: 255
• Category: Library

✦ Synopsis

Even with the advances in signal processing and digital communications, robustness to uncertain channel statistics continues to be a fundamental issue in the design and performance analysis of today's communications, radar, and sonar systems. The variability of digital communications systems consistently challenges the communications system designer, while new applications have channels that almost defy accurate modeling. As a result, parametric detectors, which are excellent when model assumptions are satisfied, do not maintain the satisfactory performance necessary for detection. This core IEEE Press reissue is the only book devoted solely to nonparametric detection - the key to maintaining good performance over a wide range of conditions. Throughout, the authors employ the classical Neyman-Pearson approach, which is widely applicable to detection problems in communications, radar, sonar, acoustics, and geophysics. Topics covered include: nonparametric detection theory, basic detection theory, one-input and two-input detectors and performance, tied observations, dependent sample performance, and engineering applications.

✦ Table of Contents

Front Cover
Introduction to Nonparametric Detection with Applications
Copyright Page
Contents
Preface
Chapter 1. Introduction to Nonparametric Detection Theory
1.1 Introduction
1.2 Nonparametric versus Parametric Detection
1.3 Historical and Mathematical Background
1.4 Outline of the Book
Chapter 2. Basic Detection Theory
2.1 Introduction
2.2 Basic Concepts
2.3 Bayes Decision Criterion
2.4 Neyman–Pearson Lemma
2.5 Receiver Operating Characteristics
2.6 Composite Hypotheses
2.7 Detector Comparison Techniques
2.8 Summary
Chapter 3. One-Input Detectors
3.1 Introduction
3.2 Parametric Detectors
3.3 Sign Detector
3.4 Wilcoxon Detector
3.5 Fisher-Yates, Normal Scores, or c1 Test
3.6 Van der Waerden’s Test
3.7 Spearman Rho Detector
3.8 Kendall Tau Detector
3.9 summary
Problems
Chapter 4. One-Input Detector Performance
4.1 Introduction
4.2 Sign Detector
4.3 Wilcoxon Detector
4.4 One-Input Detector AREs
4.5 Small Sample Performance
4.6 Summary
Problems
Chapter 5. Two-Input Detectors
5.1 Introduction
5.2 One-Input Detectors with Reference Noise Samples
5.3 Two Simultaneous Input Detectors
5.4 Summary
Problems
Chapter 6. Two-Input Detector Performance
6.1 Introduction
6.2 Asymptotic Results for Parametric Detectors
6.3 ARE of the PCC Detector
6.4 ARE of the Mann–Whitney Detector
6.5 ARE of Other Two–Input Detectors
6.6 Small Sample Results
6.7 Summary
Problems
Chapter 7. Tied Observations
7.1 Introduction
7.2 General Procedures
7.3 Specific Studies
7.4 Summary
Problems
Chapter 8. Dependent Sample Performance
8.1 Introduction
8.2 Dependence and the Constant False Alarm Rate Property
8.3 Nonparametric and Parametric Detector Performance for Correlated Inputs
8.4 Summary
Problems
Chapter 9. Engineering Applications
9.1 Introduction
9.2 Radar System Applications
9.3 Other Engineering Applications
9.4 Summary
Appendix A: Probability Density Functions
Appendix B: Mathematical Tables
Answers to Selected Problems
References
Index