Weinberg Radar Detection Theory of Sliding Window Processes

An X-Band Perspective
1. Auflage 2017
ISBN: 978-1-4987-6819-1
Verlag: CRC Press
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 400 Seiten

Constant false alarm rate detection processes are important in radar signal processing. Such detection strategies are used as an alternative to optimal Neyman-Pearson based decision rules, since they can be implemented as a sliding window process running on a radar range-Doppler map. This book examines the development of such detectors in a modern framework. With a particular focus on high resolution X-band maritime surveillance radar, recent approaches are outlined and examined. Performance is assessed when the detectors are run in real X-band radar clutter. The book introduces relevant mathematical tools to allow the reader to understand the development, and follow its implementation.

Autoren/Hrsg.

Weinberg, Graham

Fachgebiete

• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
• Technische Wissenschaften Energietechnik | Elektrotechnik Elektrotechnik
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Mikrowellentechnik

Inhaltsverzeichnis

SECTION I: PRELIMINARIES

Introduction

Purpose

Sliding Window Detectors

Range-Time Intensity Example

Historical Development

Mathematical Formulation

Detectors in Exponentially Distributed Clutter

Some Fundamental Concepts

Structure of the Book

Probability and Distribution Theory for Radar Detection

Outline

Fundamentals of Probability

Transformations

Moments

Inequalities

Jointly Distributed Random Variables

Conditional Distributions

Some Special Functions of Random Variables

Order Statistics

Uniform Distributions and Simulation

Properties of Estimators

Spherically Invariant Random Processes

Distributions for X-Band Maritime Surveillance Radar Clutter

Introduction

Early Models for Clutter

The Weibull Distribution

K-Distribution

The Pareto Class of Distributions

Pareto Type Distributions

Properties of the Pareto Distribution

Parameter Estimation

Pareto Model Adopted for Detector Development

SECTION II: FUNDAMENTAL DETECTION PROCESSES

Adaptation of Exponential Detectors to Pareto Type I Distributed Clutter

Introduction

General Considerations

The Order Statistic Detector

The Cell-Averaging Detector

The Geometric Mean Detector

Performance in Homogeneous Clutter

Effect of Interfering Targets

Clutter Transitions

Conclusions

A Transformation Approach for Radar Detector Design

Introduction

The Transformation Approach

Examples of Detector Performance

Preservation of the CFAR Property

Lomax-Distributed Clutter and Detector Performance

Modification of the General Transformed Detector

Specialisation to the Pareto Clutter Case

Performance of the New CFAR Processes

Modified Minimum Order Statistic Detector

Introduction

Transformed Order Statistic Detectors

Detector Comparison

Mathematical Analysis of Detectors

Selection of Parameter M

Performance in Homogeneous Clutter

Examples of Management of Interference

False Alarm Regulation

Conclusions

Dual Order Statistic CFAR Detectors

Introduction

Motivation and Definition of Detection Process

Specialisation to the Pareto Type I Case

Performance in Homogeneous Clutter

Management of Interfering Targets

False Alarm Regulation

Conclusions

On Goldstein’s Log-t Detector

Introduction

The Log-t Detector

An Order Statistic Based Log-t Detector

Performance in Homogeneous Clutter

Interference

False Alarm Regulation

Conclusions

SECTION III: SPECIALISED DEVELOPMENTS

Switching Based Detectors

Introduction

Development of a Switching Detector

Generalisation of the Switching Detector

Switching CFAR Detector

Performance of the SW-CFAR Detector

Conclusions

Developments in Binary Integration

Introduction

Binary Integration

Mathematical Analysis of Binary Integration

Binary Integration Parameter S

Performance in Homogeneous Clutter

Performance with Interference

Clutter Transitions

Conclusions

Detection in Range Correlated Clutter

Introduction

Decision Rule in Correlated Clutter

Mardia’s Multivariate Pareto Model

Order Statistic Decision Rule Thresholds

Performance Analysis

Analysis of the Minimum-Based Detector

Achieving CFAR in Correlated Pareto Distributed Clutter

Conclusions

SECTION IV: FURTHER CONCEPTS

Invariance and the CFAR Property

Introduction

Group Theory Basics

The Invariance Property

Some Invariant Statistics

Examples of Invariant CFAR Detectors

Performance of Invariant Detectors

Conclusions

Convergence and Approximation of the Pareto Model

Introduction

Problem Specification

Information Theory

Kullback-Leibler Divergence

Conclusions

Appendices

References

Index

Über Autor(innen)

Graham V. Weinberg completed his B.S. and Ph.D. degrees at the University of Melbourne, Australia. His doctoral thesis examined distributional approximations of stochastic processes using the Stein-Chen method. After a short period in telecommunications research at the University of Adelaide, he joined Defence Science and Technology Group, Australia. In the capacity of a scientist, he has undertaken research into radar detection issues arising from airborne high resolution X-band maritime surveillance platforms. To further continue his professional development, he has also completed a Master’s degree in signal and information processing through the University of Adelaide, Australia. His research interests include CFAR, coherent multi-look radar detection and the mathematics of radar signal processing. He has published extensively and is a member of the Institution of Engineering and Technology (IET), UK.