Muutke küpsiste eelistusi

Adaptive Detection of Multichannel Signals Exploiting Persymmetry [Kõva köide]

, , , (University of Waterloo, Canada)
  • Formaat: Hardback, 296 pages, kõrgus x laius: 234x156 mm, kaal: 630 g, 5 Tables, black and white; 59 Line drawings, black and white; 59 Illustrations, black and white
  • Ilmumisaeg: 20-Dec-2022
  • Kirjastus: CRC Press
  • ISBN-10: 1032374241
  • ISBN-13: 9781032374246
Teised raamatud teemal:
Adaptive Detection of Multichannel Signals Exploiting PersymmetrySuurem pilt
  • Formaat: Hardback, 296 pages, kõrgus x laius: 234x156 mm, kaal: 630 g, 5 Tables, black and white; 59 Line drawings, black and white; 59 Illustrations, black and white
  • Ilmumisaeg: 20-Dec-2022
  • Kirjastus: CRC Press
  • ISBN-10: 1032374241
  • ISBN-13: 9781032374246
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781032374246.html
Märksõnad:
This book offers a systematic presentation of persymmetric adaptive detection, including detector derivations and the definition of key concepts, followed by detailed discussion relating to theoretical underpinnings, design methodology, design considerations, and techniques enabling its practical implementation.

The received data for modern radar systems are usually multichannel, namely, vector-valued, or even matrix-valued. Multichannel signal detection in Gaussian backgrounds is a fundamental problem for radar applications. With an overarching focus on persymmetric adaptive detectors, this book presents the mathematical models and design principles necessary for analyzing the behavior of each kind of persymmetric adaptive detector. Building upon that, it also introduces new design approaches and techniques that will guide engineering students as well as radar engineers toward efficient detector solutions, especially in challenging sample-starved environments where training data are limited.

This book will be of interest to students, scholars, and engineers in the field of signal processing. It will be especially useful for those who have a solid background in statistical signal processing, multivariate statistical analysis, matrix theory, and mathematical analysis.
List of Abbreviations
xiii
List of Symbols
xv
1 Basic Concept
1(18)
1.1 Multichannel Radar
1(1)
1.2 Adaptive Detection of Multichannel Signal
1(4)
1.3 Persymmetric Structure of Covariance Matrix
5(3)
1.4 Organization and Outline of the Book
8(1)
1.A Detector Design Criteria
9(4)
1.A.1 Nuisance Parameter
10(1)
1.A.1.1 Rao Test
10(1)
1.A.1.2 Wald Test
11(1)
1.A.1.3 GLRT
11(1)
1.A.2 No Nuisance Parameter
11(1)
1.A.2.1 Rao Test
11(1)
1.A.2.2 Wald Test
12(1)
1.A.2.3 GLRT
12(1)
Bibliography
13(6)
2 Output SINR Analysis
19(32)
2.1 Problem Formulation
20(3)
2.1.1 Unstructured SMI Beamformer
21(1)
2.1.1.1 Matched Case
21(1)
2.1.1.2 Mismatched Case
22(1)
2.1.2 Persymmetric SMI Beamformer
22(1)
2.2 Average SINR in Matched Case
23(2)
2.3 Average SINR in Mismatched Cases
25(3)
2.3.1 Homogeneous Case
25(2)
2.3.2 Non-Homogeneous Case
27(1)
2.4 Simulation Results
28(3)
2.A Derivation of E(pgg)
31(4)
2.B Proof of Theorem 2.3.1
35(7)
2.C Derivations of (2.B.22)
42(2)
2.D Derivations of (2.B.28)
44(1)
2.E Derivations of (2.B.36)
44(1)
2.F Derivation of E(ww†) in the Mismatched Case
45(3)
Bibliography
48(3)
3 Invar iance Issues under Per symmetry
51(30)
3.1 Preliminary Theory
51(1)
3.2 Homogeneous Environment
52(10)
3.2.1 Stochastic Representation
56(2)
3.2.2 Invariant Detectors
58(2)
3.2.3 Statistical Characterization
60(1)
3.2.3.1 LRT-Based Decision Schemes
61(1)
3.3 Partially Homogeneous Environment
62(3)
3.3.1 Invariant Detectors for Partially Homogeneous Scenarios
63(2)
3.A Proof of Theorem 3.2.1
65(4)
3.B Derivation of (3.38)
69(1)
3.C Proof of Theorem 3.2.2
70(1)
3.D Proof of Theorem 3.2.3
71(5)
3.E Proof of Theorem 3.2.5
76(1)
3.F Proof of Theorem 3.3.1
77(1)
Bibliography
78(3)
4 Persymmetric Adaptive Subspace Detector
81(20)
4.1 Problem Formulation
81(1)
4.2 Persymmetric One-Step GLRT
82(3)
4.3 Threshold Setting
85(5)
4.3.1 Transformation from Complex Domain to Real Domain
86(1)
4.3.2 Statistical Characterizations
87(1)
4.3.2.1 Equivalent Form of XTPX
87(1)
4.3.2.2 Equivalent Form of XTPX
88(1)
4.3.2.3 Statistical Distribution of A
89(1)
4.3.3 Probability of False Alarm
89(1)
4.4 Numerical Examples
90(1)
4.A Derivations of (4.13)
91(2)
4.B Derivations of (4.38)
93(3)
4.C Derivations of (4.62)
96(2)
Bibliography
98(3)
5 Persymmetric Detectors with Enhanced Rejection Capabilities
101(12)
5.1 Problem Formulation
101(2)
5.2 Detector Design
103(3)
5.2.1 Persymmetric Rao Test
103(1)
5.2.2 Persymmetric GLRT
104(2)
5.3 Numerical Examples
106(2)
5.A Derivations of (5.32)
108(3)
Bibliography
111(2)
6 Distributed Target Detection in Homogeneous Environments
113(32)
6.1 Persymmetric One-Step GLRT
114(9)
6.1.1 Detector Design
114(2)
6.1.2 Analytical Performance
116(1)
6.1.2.1 Transformation from Complex Domain to Real Domain
116(1)
6.1.2.2 Statistical Properties
117(2)
6.1.2.3 Detection Probability
119(3)
6.1.2.4 Probability of False Alarm
122(1)
6.2 Persymmetric Two-Step GLRT
123(7)
6.2.1 Detector Design
123(1)
6.2.2 Analytical Performance
124(1)
6.2.2.1 Statistical Properties
125(1)
6.2.2.2 Probability of False Alarm
126(1)
6.2.2.3 Detection Probability
127(3)
6.3 Numerical Examples
130(6)
6.A Derivations of (6.31)
136(3)
6.B Derivations of (6.39)
139(1)
6.C Derivations of (6.69)
140(2)
6.D Proof of Theorem 6.2.1
142(1)
Bibliography
142(3)
7 Robust Detection in Homogeneous Environments
145(16)
7.1 Problem Formulation
145(1)
7.2 Detection Design
146(7)
7.2.1 GLRT Criterion
148(1)
7.2.1.1 One-Step GLRT
148(1)
7.2.1.2 Two-Step GLRT
148(1)
7.2.2 Wald Criterion
149(1)
7.2.2.1 One-Step Wald Test
149(2)
7.2.2.2 Two-Step Wald Test
151(1)
7.2.3 Rao Criterion
152(1)
7.3 Numerical Examples
153(4)
7.A Derivations of (7.36)
157(1)
Bibliography
158(3)
8 Adaptive Detection with Unknown Steering Vector
161(22)
8.1 Problem Formulation
161(1)
8.2 Per-SNT Detector
162(5)
8.2.1 Detector Design
162(2)
8.2.2 Threshold Setting for Per-SNT
164(2)
8.2.2.1 Transformation from Complex Domain to Real Domain
166(1)
8.2.2.2 Probability of False Alarm for Per-SNT
166(1)
8.3 Per-GLRT Detector
167(3)
8.3.1 Detector Design
167(2)
8.3.2 Threshold Setting for Per-GLRT
169(1)
8.4 Numerical Examples
170(9)
8.4.1 Probability of False Alarm
170(1)
8.4.2 Detection Performance
170(7)
8.4.3 Measured Data
177(2)
8.A Derivations of (8.13)
179(1)
8.B Proof of Theorem 8.2.1
180(1)
8.C Derivations of (8.41) and (8.42)
181(1)
8.D Derivation of (8.64)
182(1)
8 E CFARness of the Per-GLRT
183(4)
Bibliography
184(3)
9 Adaptive Detection in Interference
187(12)
9.1 Problem Formulation
187(1)
9.2 GLRT Detection
188(3)
9.2.1 One-Step GLRT
188(2)
9.2.2 Two-Step GLRT
190(1)
9.3 Probability of False Alarm for 1S-PGLRT-I
191(7)
9.3.1 p is 1
194(1)
9.3.2 p is 2
194(1)
9.3.3 p is 3
195(1)
9.3.4 p is 4
195(1)
9.3.5 H = 1
196(1)
9.3.6 H = 2
196(1)
9.3.7 Arbitrary H and p
197(1)
9.4 Numerical Examples
198(1)
9 A Derivations of (9.47)
199(6)
Bibliography
202(3)
10 Adaptive Detection in Partially Homogeneous Environments
205(12)
10.1 Detector Design
205(5)
10.1.1 One-Step GLRT
205(2)
10.1.2 Two-Step GLRT
207(2)
10.1.3 Rao and Wald Tests
209(1)
10.2 Numerical Examples
210(4)
Bibliography
214(3)
11 Robust Detection in Partially Homogeneous Environments
217(18)
11.1 Problem Formulation
217(2)
11.2 Robust Detection
219(5)
11.2.1 GLRT
219(4)
11.2.2 Wald Test
223(1)
11.2.3 Rao Test
224(1)
11.3 CFARness Analysis
224(2)
11.4 Numerical Examples
226(2)
11.A Derivations of (11.47)
228(4)
Bibliography
232(3)
12 Joint Exploitation of Persymmetry and Symmetric Spectrum
235(18)
12.1 Problem Formulation
235(3)
12.2 Rao Test
238(2)
12.3 Two-Step GLRT and Wald Test
240(3)
12.3.1 Homogeneous Environment
240(2)
12.3.2 Partially Homogeneous Environment
242(1)
12.4 Numerical Examples
243(6)
12.4.1 Homogeneous Environment
244(1)
12.4.2 Partially Homogeneous Environment
245(4)
Bibliography
249(4)
13 Adaptive Detection after Covariance Matrix Classification
253(12)
13.1 Problem Formulation
253(1)
13.2 Architecture Design
254(5)
13.2.1 Classification Stage
255(1)
13.2.2 Detection Stage
256(1)
13.2.2.1 Detector under B\
256(1)
13.2.2.2 Detector under H2
256(1)
13.2.2.3 Detector under H3
257(1)
13.2.2.4 Detector under H4
257(1)
13.2.2.5 Detector under H5
257(1)
13.2.2.6 Detector under H6
258(1)
13.2.3 Threshold Setting
258(1)
13.3 Numerical Results
259(4)
Bibliography
263(2)
14 MIMO Radar Target Detection
265
14.1 Persymmetric Detection in Colocated MIMO Radar
266(14)
14.1.1 Problem Formulation
266(2)
14.1.2 Adaptive Detector
268(3)
14.1.3 Analytical Performance
271(1)
14.1.3.1 Transformation from Complex Domain to Real Domain
271(1)
14.1.3.2 Statistical Properties
272(1)
14.1.3.3 Detection Probability
272(3)
14.1.3.4 Probability of False Alarm
275(1)
14.1.4 Numerical Examples
275(5)
14.2 Persymmetric Detection in Distributed MIMO Radar
280(9)
14.2.1 Signal Model
280(2)
14.2.2 Persymmetric GLRT Detector
282(1)
14.2.2.1 Detector Design
282(2)
14.2.2.2 Performance Analysis
284(2)
14.2.3 Persymmetric SMI Detector
286(1)
14.2.4 Simulations Results
287(2)
14.A Derivation of (14.94)
289(2)
14.B Equivalent Transformation of A
291(1)
Bibliography
292
Jun Liu is an Associate Professor with the Department of Electronic Engineering and Information Science, University of Science and Technology of China. Dr. Liu is a member of the Sensor Array and Multichannel (SAM) Technical Committee, IEEE Signal Processing Society.

Danilo Orlando is an Associate Professor at Università degli Studi "Niccolò Cusano". His research interests focus on signal processing for radar and sonar systems. He has co-authored more than 150 publications in international journals, conferences, and books.

Chengpeng Hao is a Professor at the Institute of Acoustics, Chinese Academy of Sciences. His research interests are in the fields of statistical signal processing, array signal processing, radar, and sonar engineering. He has authored and co-authored more than 100 scientific publications in international journals and conferences.

Weijian Liu is an Associate Professor with the Wuhan Electronic Information Institute, China. His research interests include multichannel signal detection and statistical and array signal processing.