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E-grāmata: Waveform Design for Active Sensing Systems: A Computational Approach

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(University of Florida), (Uppsala Universitet, Sweden), (University of Florida)
  • Formāts: PDF+DRM
  • Izdošanas datums: 12-Jul-2012
  • Izdevniecība: Cambridge University Press
  • Valoda: eng
  • ISBN-13: 9781139369169
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Waveform Design for Active Sensing Systems: A Computational ApproachPalielināt
  • Formāts: PDF+DRM
  • Izdošanas datums: 12-Jul-2012
  • Izdevniecība: Cambridge University Press
  • Valoda: eng
  • ISBN-13: 9781139369169
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With a focus on developing computational algorithms for examining waveform design in diverse active sensing applications, this guide is ideal for researchers and practitioners in the field. The three parts conveniently correspond to the three categories of desirable waveform properties: good aperiodic correlations, good periodic correlations and beampattern matching. The book features various application examples of using the newly designed waveforms, including radar imaging, channel estimation for communications, an ultrasound system for breast cancer treatment and covert underwater communications. In addition to numerical results, the authors present theoretical analyses describing lower bounds or limitations of performance. Focusing on formulating practical problems mathematically and solving the mathematical problems using efficient and effective optimization techniques, the text pays particular attention to developing easy-to-use computational approaches. Most algorithms are accompanied by a table clearly detailing iteration steps and corresponding MATLAB codes are available on the companion website.

Papildus informācija

Ideal for researchers and practitioners looking to develop and use computational algorithms for waveform design in diverse active sensing applications.
Preface xi
Notation xiii
Abbreviations xiv
1 Introduction
1(14)
1.1 Signal model
2(2)
1.2 Design metrics
4(2)
1.3 Review of existing waveforms
6(9)
Part I Aperiodic correlation synthesis
15(132)
2 Single aperiodic sequence design
17(22)
2.1 Cyclic algorithm-new (CAN)
18(3)
2.2 Weighted cyclic algorithm-new (WeCAN)
21(4)
2.3 Numerical examples
25(9)
2.3.1 Integrated sidelobe level (ISL) design
25(1)
2.3.2 Weighted integrated sidelobe level (WISL) design
25(5)
2.3.3 Channel estimation in communications
30(1)
2.3.4 Quantization effects
31(3)
2.4 Conclusions
34(5)
Appendix 2A Connections with a phase-retrieval algorithm
35(4)
3 Aperiodic sequence set design
39(28)
3.1 The Multi-CAN algorithm
40(3)
3.2 The Multi-WeCAN algorithm
43(3)
3.3 The Multi-CA-original (Multi-CAO) algorithm
46(2)
3.4 Numerical examples
48(17)
3.4.1 Multi-CAN
48(5)
3.4.2 Multi-WeCAN
53(2)
3.4.3 Multi-WeCAN continued
55(2)
3.4.4 Quantization effects
57(1)
3.4.5 Synthetic aperture radar (SAR) imaging
57(8)
3.5 Conclusions
65(2)
Appendix 3A Proof of Equation (3.28)
65(1)
Appendix 3B Proof of Equation (3.47)
66(1)
4 Lower bounds for aperiodic sequences
67(7)
4.1 Bound derivation
67(2)
4.2 Approaching the bound
69(4)
4.3 Conclusions
73(1)
5 Stopband constraint case
74(14)
5.1 Stopband CAN (SCAN)
75(2)
5.2 Weighted SCAN (WeSCAN)
77(3)
5.3 Numerical examples
80(7)
5.3.1 SCAN
80(2)
5.3.2 WeSCAN
82(1)
5.3.3 Relaxed amplitude constraint
82(5)
5.3.4 Using a different frequency formulation
87(1)
5.4 Conclusions
87(1)
6 Ambiguity function (AF)
88(18)
6.1 AF properties
88(9)
6.2 Discrete-AF
97(2)
6.3 Minimizing the discrete-AF sidelobes
99(2)
6.4 Conclusions
101(5)
Appendix 6A Wideband ambiguity function
102(4)
7 Cross ambiguity function (CAF)
106(17)
7.1 Discrete-CAF synthesis
106(9)
7.1.1 The proposed algorithm
107(2)
7.1.2 Numerical examples
109(6)
7.2 CAF synthesis
115(6)
7.2.1 The proposed algorithm
116(2)
7.2.2 Numerical examples
118(3)
7.3 Conclusions
121(2)
Appendix 7A Constant volume property of discrete-CAF
121(2)
8 Joint design of transmit sequence and receive filter
123(24)
8.1 Data model and problem formulation
124(2)
8.2 A gradient approach
126(2)
8.3 A frequency-domain approach
128(6)
8.4 Specialization for matched filtering
134(2)
8.5 Numerical examples
136(6)
8.5.1 Spot jamming
137(3)
8.5.2 Barrage jamming
140(2)
8.5.3 Robust design
142(1)
8.6 Conclusions
142(5)
Appendix 8A Proof of Equation (8.25)
145(1)
Appendix 8B Lagrange approach
146(1)
Part II Periodic correlation synthesis
147(38)
9 Single periodic sequence design
149(9)
9.1 Design criteria
150(3)
9.2 The periodic CAN (PeCAN) algorithm
153(1)
9.3 Numerical examples
154(1)
9.4 Conclusions
155(3)
Appendix 9A Proof of Equation (9.9)
155(3)
10 Periodic sequence set design
158(10)
10.1 The Multi-PeCAO algorithm
159(2)
10.2 The Multi-PeCAN algorithm
161(2)
10.3 Numerical examples
163(4)
10.3.1 Multi-PeCAO
163(2)
10.3.2 Multi-PeCAN
165(2)
10.4 Conclusions
167(1)
11 Lower bounds for periodic sequences
168(7)
11.1 Bound derivation
168(3)
11.2 Optimal ISL sequence sets
171(2)
11.3 Numerical examples
173(1)
11.4 Conclusions
174(1)
12 Periodic ambiguity function (PAF)
175(10)
12.1 PAF properties
176(1)
12.2 Discrete-PAF
177(5)
12.3 Minimizing the discrete-PAF sidelobes
182(2)
12.4 Conclusions
184(1)
Part III Transmit beampattern synthesis
185(60)
13 Narrowband beampattern to covariance matrix
187(26)
13.1 Problem formulation
188(2)
13.2 Optimal designs
190(7)
13.2.1 Maximum power design for unknown target locations
190(1)
13.2.2 Maximum power design for known target locations
191(2)
13.2.3 Beampattern matching design
193(3)
13.2.4 Minimum sidelobe beampattern design
196(1)
13.2.5 Phased-array beampattern design
197(1)
13.3 Numerical examples
197(14)
13.3.1 Beampattern matching design
198(7)
13.3.2 Minimum sidelobe beampattern design
205(6)
13.4 Conclusions
211(2)
Appendix 13A Covariance matrix rank
211(2)
14 Covariance matrix to waveform
213(9)
14.1 Problem formulation
213(2)
14.2 Cyclic algorithm for signal synthesis
215(1)
14.3 Numerical examples
216(3)
14.4 Conclusions
219(3)
15 Wideband transmit beampattern synthesis
222(23)
15.1 Problem formulation
222(3)
15.2 The proposed design methodology
225(4)
15.2.1 Beampattern to spectrum
226(1)
15.2.2 Spectrum to waveform
227(2)
15.3 Numerical examples
229(13)
15.3.1 The idealized time-delayed case
229(1)
15.3.2 A narrow mainbeam
230(3)
15.3.3 Two mainbeams
233(1)
15.3.4 A wide mainbeam
233(9)
15.4 Conclusions
242(3)
Appendix 15A Narrowband transmit beampattern
242(1)
Appendix 15B Receive beampattern
243(2)
Part IV Diverse application examples
245(56)
16 Radar range and range-Doppler imaging
247(12)
16.1 Problem formulation
247(2)
16.2 Receiver design
249(2)
16.2.1 Matched filter
249(1)
16.2.2 Instrumental variable (IV) receive filter
250(1)
16.3 Iterative adaptive approach (IAA)
251(1)
16.4 Numerical examples
252(3)
16.4.1 Negligible Doppler example
252(3)
16.4.2 Non-negligible Doppler example
255(1)
16.5 Conclusions
255(4)
17 Ultrasound system for hyperthermia treatment of breast cancer
259(8)
17.1 Waveform diversity based ultrasound hyperthermia
260(2)
17.2 Numerical results
262(4)
17.3 Conclusions
266(1)
18 Covert underwater acoustic communications-coherent scheme
267(13)
18.1 Problem formulation
268(1)
18.2 Spreading waveform synthesis
269(4)
18.3 Numerical examples
273(6)
18.4 Conclusions
279(1)
19 Covert underwater acoustic communications-noncoherent scheme
280(21)
19.1 RAKE energy-based detection of orthogonal signals
280(3)
19.2 RAKE demodulator for DPSK signals
283(4)
19.3 The impact of P and R on performance and an enhanced RAKE scheme
287(3)
19.3.1 Impact of P and R on the BER performance
287(1)
19.3.2 RAKE reception based on the principal arrival
288(2)
19.4 Numerical examples
290(10)
19.4.1 Binary orthogonal modulation
290(6)
19.4.2 DPSK modulation
296(4)
19.5 Conclusions
300(1)
References 301(10)
Index 311
Hao He received his PhD from the Department of Electrical and Computer Engineering at the University of Florida, USA, in 2011. His student papers won awards at the IEEE 13th DSP Workshop and 5th SPE Workshop in 2009 and at the 2nd International Workshop on Cognitive Information Processing in 2010. Jian Li is a professor at the Department of Electrical and Computer Engineering, University of Florida, USA, and a Fellow of IEEE and IET. She has published three books, four book chapters and some 400 papers in archival journals and conference records. She is a co-author of the paper that received the M. Barry Carlton Award for the best paper published in IEEE Transactions on Aerospace and Electronic Systems in 2005. Petre Stoica is a professor at the Department of Information Technology at Uppsala University, Sweden, a member of the Royal Swedish Academy of Engineering Sciences and the European Academy of Sciences, an honorary member of the Romanian Academy and a Fellow of the Royal Statistical Society, IEEE and EURASIP. He has published 10 books, 15 book chapters and some 700 papers in archival journals and conference records and has won several awards of IEEE, IEE and EURASIP.
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