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E-raamat: Radar Detection Theory of Sliding Window Processes

(Defence Science and Technology Group (DSTG), Australia)
  • Formaat: 400 pages
  • Ilmumisaeg: 27-Sep-2017
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781351650069
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Radar Detection Theory of Sliding Window ProcessesSuurem pilt
  • Formaat: 400 pages
  • Ilmumisaeg: 27-Sep-2017
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781351650069
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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.

Dedication v
Preface vii
Acknowledgements ix
List of Figures
xvii
List of Tables
xxix
Acronyms and Symbols xxxi
SECTION I PRELIMINARIES
1(74)
1 Introduction
3(18)
1.1 Purpose
3(2)
1.2 Sliding Window Detectors
5(2)
1.3 Range-Time Intensity Example
7(3)
1.4 Historical Development
10(3)
1.5 Mathematical Formulation
13(2)
1.6 Detectors in Exponentially Distributed Clutter
15(2)
1.7 Some Fundamental Concepts
17(1)
1.8 Structure of the Book
18(3)
2 Probability and Distribution Theory for Radar Detection
21(30)
2.1 Outline
21(1)
2.2 Fundamentals of Probability
22(7)
2.3 Transformations
29(2)
2.4 Moments
31(5)
2.5 Inequalities
36(1)
2.6 Jointly Distributed Random Variables
37(1)
2.7 Conditional Distributions
38(1)
2.8 Some Special Functions of Random Variables
39(2)
2.9 Order Statistics
41(2)
2.10 Uniform Distributions and Simulation
43(2)
2.11 Properties of Estimators
45(2)
2.12 Spherically Invariant Random Processes
47(4)
3 Distributions for X-Band Maritime Surveillance Radar Clutter
51(24)
3.1 Introduction
51(1)
3.2 Early Models for Clutter
52(1)
3.3 The Weibull Distribution
53(1)
3.4 K-Distribution
54(2)
3.5 The Pareto Class of Distributions
56(6)
3.6 Pareto Type Distributions
62(2)
3.7 Properties of the Pareto Distribution
64(7)
3.8 Parameter Estimation
71(2)
3.9 Pareto Model Adopted for Detector Development
73(2)
SECTION II FUNDAMENTAL DETECTION PROCESSES
75(126)
4 Adaptation of Exponential Detectors to Pareto Type I Distributed Clutter
77(26)
4.1 Introduction
77(1)
4.2 General Considerations
78(2)
4.3 The Order Statistic Detector
80(2)
4.4 The Cell-Averaging Detector
82(1)
4.5 The Geometric Mean Detector
83(1)
4.6 Performance in Homogeneous Clutter
84(2)
4.7 Effect of Interfering Targets
86(9)
4.8 Clutter Transitions
95(6)
4.9 Conclusions
101(2)
5 A Transformation Approach for Radar Detector Design
103(34)
5.1 Introduction
103(1)
5.2 The Transformation Approach
104(4)
5.3 Examples of Detector Performance
108(2)
5.4 Preservation of the CFAR Property
110(4)
5.5 Lomax-Distributed Clutter and Detector Performance
114(6)
5.6 Modification of the General Transformed Detector
120(2)
5.7 Specialisation to the Pareto Clutter Case
122(3)
5.8 Performance of the New CFAR Processes
125(11)
5.8.1 Performance in Homogeneous Clutter
125(5)
5.8.2 Performance in the Presence of Interference
130(1)
5.8.3 Clutter Transitions
130(6)
5.9 Conclusions
136(1)
6 Modified Minimum Order Statistic Detector
137(18)
6.1 Introduction
137(1)
6.2 Transformed Order Statistic Detectors
138(1)
6.3 Detector Comparison
139(2)
6.4 Mathematical Analysis of Detectors
141(4)
6.5 Selection of Parameter M
145(1)
6.6 Performance in Homogeneous Clutter
146(1)
6.7 Examples of Management of Interference
146(4)
6.8 False Alarm Regulation
150(3)
6.9 Conclusions
153(2)
7 Dual Order Statistic CFAR Detectors
155(26)
7.1 Introduction
155(1)
7.2 Motivation and Definition of Detection Process
155(4)
7.3 Specialisation to the Pareto Type I Case
159(4)
7.4 Performance in Homogeneous Clutter
163(4)
7.5 Management of Interfering Targets
167(4)
7.6 False Alarm Regulation
171(9)
7.7 Conclusions
180(1)
8 On Goldstein's Log-t Detector
181(20)
8.1 Introduction
181(1)
8.2 The Log-t Detector
182(1)
8.3 An Order Statistic Based Log-t Detector
183(3)
8.4 Performance in Homogeneous Clutter
186(3)
8.5 Interference
189(3)
8.6 False Alarm Regulation
192(7)
8.7 Conclusions
199(2)
SECTION III SPECIALISED DEVELOPMENTS
201(82)
9 Switching Based Detectors
203(22)
9.1 Introduction
203(1)
9.2 Development of a Switching Detector
204(6)
9.3 Generalisation of the Switching Detector
210(2)
9.4 Switching CFAR Detector
212(4)
9.5 Performance of the SW-CFAR Detector
216(8)
9.5.1 Performance in Homogeneous Clutter
216(2)
9.5.2 Performance with Interference
218(1)
9.5.3 False Alarm Regulation
218(6)
9.6 Conclusions
224(1)
10 Developments in Binary Integration
225(28)
10.1 Introduction
225(1)
10.2 Binary Integration
226(2)
10.3 Mathematical Analysis of Binary Integration
228(7)
10.4 Binary Integration Parameter S
235(2)
10.5 Performance in Homogeneous Clutter
237(4)
10.6 Performance with Interference
241(8)
10.7 Clutter Transitions
249(3)
10.8 Conclusions
252(1)
11 Detection in Range Correlated Clutter
253(30)
11.1 Introduction
253(1)
11.2 Decision Rule in Correlated Clutter
254(2)
11.3 Mardia's Multivariate Pareto Model
256(3)
11.4 Order Statistic Decision Rule Thresholds
259(2)
11.5 Performance Analysis
261(15)
11.5.1 Performance in Homogeneous Clutter
262(5)
11.5.2 Performance with Interference in the CRP
267(4)
11.5.3 False Alarm Considerations
271(5)
11.6 Analysis of the Minimum-Based Detector
276(5)
11.7 Achieving CFAR in Correlated Pareto Distributed Clutter
281(1)
11.8 Conclusions
282(1)
SECTION IV FURTHER CONCEPTS
283(42)
12 Invariance and the CFAR Property
285(26)
12.1 Introduction
285(1)
12.2 Group Theory Basics
286(1)
12.3 The Invariance Property
287(1)
12.4 Some Invariant Statistics
288(3)
12.5 Examples of Invariant CFAR Detectors
291(6)
12.6 Performance of Invariant Detectors
297(13)
12.6.1 Homogeneous Clutter
297(6)
12.6.2 Interference
303(3)
12.6.3 False Alarm Regulation
306(4)
12.7 Conclusions
310(1)
13 Convergence and Approximation of the Pareto Model
311(14)
13.1 Introduction
311(1)
13.2 Problem Specification
312(1)
13.3 Information Theory
313(2)
13.4 Kullback-Leibler Divergence
315(8)
13.5 Conclusions
323(2)
Appendices
325(22)
A Neyman-Pearson Lemma
327(2)
B CA- and OS-CFAR in Exponentially Distributed Clutter
329(4)
C Radar Cross Section and Target Models
333(2)
D Classical Non-Coherent Integrators
335(4)
E Ideal Detectors
339(8)
References 347(14)
Index 361
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 Masters 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.
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