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E-raamat: Multidimensional Radar Imaging, Volume 1

Edited by (University of Pisa, Department of Information Engineering, Italy)
  • Formaat: PDF+DRM
  • Sari: Radar, Sonar and Navigation
  • Ilmumisaeg: 13-Dec-2019
  • Kirjastus: Institution of Engineering and Technology
  • Keel: eng
  • ISBN-13: 9781785618086
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Multidimensional Radar Imaging, Volume 1Suurem pilt
  • Formaat: PDF+DRM
  • Sari: Radar, Sonar and Navigation
  • Ilmumisaeg: 13-Dec-2019
  • Kirjastus: Institution of Engineering and Technology
  • Keel: eng
  • ISBN-13: 9781785618086
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This book introduces a new framework which addresses the problem of radar imaging and target recognition as it jointly looks at optimising the use of multiple channels to significantly outperform classical radar imaging systems. It has been used in the military within NATO for the last few years and the technology is now declassified.



Synthetic aperture radar and inverse synthetic aperture radar (SAR/ISAR) images have been largely used for monitoring small to large areas and more specifically for target recognition/identification. However, the technology has limitations due to the use of classical monostatic, single channel, single frequency and single polarization systems. To overcome these limitations, solutions have been proposed that show the benefit of using multiple frequencies, spatial channels, polarisations and perspective, in one word multi-dimensional radar imaging systems when dealing with non-cooperative targets.

Multidimensional Radar Imaging introduces a new framework within which to address the problem of radar imaging and target recognition as it jointly looks at optimising the use of multiple channels to significantly outperform classical radar imaging systems.

It has been used in the military within NATO for the last few years and the technology is now declassified.

Topics covered include three-dimensional ISAR; STAP-ISAR; wide-band multi-look passive ISAR; radar tomography; multistatic PCL-SAR; fusion of multistatic ISAR images with large angular separation; rotor blade parameter estimation with multichannel passive radar; multistatic 3D ISAR imaging of maritime targets; challenges of semi-cooperative bi/multistatic SAR using Cosmo SkyMEd as an illuminator; and lessons learnt from the NATO SET-196 RTG on multi-channel/multi-static radar imaging of non-cooperative targets.

About the editors xi
Foreword xiii
1 Introduction
1(12)
Marco Martorella
David Greig
1.1 Background
1(3)
1.2 Multidimensional radar
4(9)
1.2.1 Multistatic radar imaging systems
5(1)
1.2.2 Multichannel radar imaging systems
6(1)
1.2.3 Multi-polarisation radar imaging systems
7(1)
1.2.4 Multi-frequency radar imaging systems
7(1)
1.2.5 Systems design considerations
8(1)
1.2.6 Concluding remarks
9(1)
1.2.7 Book organisation
10(3)
Part I Multidimensional radar imaging algorithms
13(242)
2 Three-dimensional inverse synthetic aperture radar
15(32)
Daniele Stagliand
Federica Salvetti
Elisa Giusti
Marco Martorella
2.1 Introduction
15(2)
2.2 Algorithm description
17(11)
2.2.1 Multichannel ISAR signal model
18(1)
2.2.2 System geometry
18(1)
2.2.3 Received signal modelling
18(3)
2.2.4 Multichannel CLEAN algorithm
21(2)
2.2.5 3D reconstruction processing
23(5)
2.3 Performance analysis
28(2)
2.3.1 Scatterers realignment
28(1)
2.3.2 Soft assignment
29(1)
2.3.3 Performance indicators
29(1)
2.4 Results
30(12)
2.4.1 Simulated results
30(7)
2.4.2 Experimental results
37(5)
2.5 Conclusion
42(5)
References
43(4)
3 STAP-ISAR
47(62)
Samuele Gelli
Alessio Bacci
Marco Martorella
3.1 Mathematical background
47(13)
3.1.1 Multichannel ISAR signal model
48(3)
3.1.2 High-resolution imaging of noncooperative moving targets
51(4)
3.1.3 Clutter model
55(5)
3.2 Space-time adaptive processing for clutter suppression
60(13)
3.2.1 Joint SDAP IS AR
60(8)
3.2.2 Joint E-SDAP ISAR
68(5)
3.3 Results
73(32)
3.3.1 SDAP-ISAR results
73(15)
3.3.2 E-SDAP ISAR results
88(17)
3.4 Conclusion
105(4)
References
105(4)
4 Wide-band multi-look passive ISAR
109(32)
Elisa Giusti
Marco Martorella
Fabrizio Berizzi
Amerigo Capria
4.1 Introduction
109(2)
4.2 Data pre-processing
111(8)
4.2.1 Target extraction
112(3)
4.2.2 Merging of RD maps and ISAR data formation
115(4)
4.3 ISAR image processing
119(4)
4.3.1 Conventional ISAR imaging
119(2)
4.3.2 CS-based ISAR imaging
121(2)
4.4 Results
123(14)
4.4.1 Cooperative targets - WUT system
123(7)
4.4.2 Non-cooperative targets - SMARP
130(7)
4.5 Conclusions
137(4)
References
139(2)
5 Radar tomography
141(48)
Hai-Tan Tran
Lorenzo Lo Monte
5.1 Introduction
141(6)
5.1.1 Adaptability
146(1)
5.1.2 Modularity
146(1)
5.1.3 Graceful degradation
146(1)
5.2 Tomographic image formation
147(32)
5.2.1 Signal models
147(3)
5.2.2 Fourier-based methods
150(14)
5.2.3 Matrix-based methods
164(7)
5.2.4 Multistats Doppler-radar tomography
171(8)
5.3 Practical considerations
179(3)
5.3.1 Considerations with system geometry
179(3)
5.3.2 Considerations for signal processing
182(1)
5.4 Concluding remarks
182(7)
Appendix
184(1)
A Theoretical image resolution limits
184(1)
References
185(4)
6 Multistats PCL-SAR
189(46)
Diego Cristallini
Philipp Wojaczek
Ingo Walterscheid
6.1 Introduction
189(2)
6.2 Signal processing for PCL-SAR based on DVB-T
191(16)
6.2.1 Structure of DVB-T signal
192(1)
6.2.2 Received DVB-T signal model
192(1)
6.2.3 Synchronization and reference signal reconstruction
193(3)
6.2.4 Range compression in PCL-SAR
196(2)
6.2.5 Image formation
198(2)
6.2.6 Challenges for airborne PCL
200(7)
6.3 Multi-PCL-SAR for improved range resolution
207(15)
6.3.1 Range resolution improvement principle
207(4)
6.3.2 Scenario for multi-PCL-SAR
211(1)
6.3.3 Simulation of DVB-T range pulse response
212(7)
6.3.4 Optimal trajectories for multi-PCL-SAR
219(3)
6.4 Experimental verification
222(5)
6.4.1 Scenario definition
222(3)
6.4.2 Image results of PCL-SAR
225(2)
6.5 Conclusions
227(8)
Acknowledgments
229(1)
Glossary
229(1)
References
230(5)
7 Sparsity-driven multistats ISAR image reconstruction
235(20)
Stefan Brisken
7.1 Constraints
236(2)
7.1.1 Spatial decorrelation
236(2)
7.1.2 Foreshortening effect
238(1)
7.2 Problem formulation
238(3)
7.3 Reconstruction
241(3)
7.4 A simulated example
244(3)
7.5 Experimental results
247(2)
7.6 Conclusion
249(6)
References
250(5)
Part II Practical feasibility and applications
8 Rotor blade parameter estimation with multichannel passive radar
255(32)
Marcin Kamil Bqczyk
Jacek Misiurewicz
8.1 Introduction
255(2)
8.1.1 Problem formulation
255(1)
8.1.2 Methods for helicopter classification
256(1)
8.1.3 Recognition of the propeller aircraft or other rotary-wing aircraft
257(1)
8.2 A geometric model of the helicopter
257(5)
8.2.1 The echo of the helicopter's fuselage
257(1)
8.2.2 The main rotor
258(3)
8.2.3 The tail rotor
261(1)
8.2.4 The rotor parameters
262(1)
8.3 A model of the received echo signal
262(7)
8.3.1 A generic model of the echo signal
262(1)
8.3.2 A model of the main rotor blade echo
263(5)
8.3.3 A tail rotor blade echo model
268(1)
8.4 Method for determining rotor parameters
269(7)
8.4.1 Main rotor parameters
271(4)
8.4.2 Tail rotor parameters
275(1)
8.5 Main rotor imaging algorithm based on the target echo spectrogram
276(3)
8.6 Live signal processing experiment
279(5)
8.6.1 Measurement campaign
279(1)
8.6.2 Data pre-processing
279(2)
8.6.3 Results
281(3)
8.7 Conclusions and result discussion
284(3)
References
284(3)
9 Multistats 3D ISAR imaging of maritime targets
287(24)
Federica Salvetti
Elisa Giusti
Daniele Stagliand
Marco Martorella
9.1 Multiview 3D MS AR image fusion
289(6)
9.1.1 3D fusion
292(3)
9.2 Multiview 3D InlSAR image fusion in experimental scenarios
295(13)
9.2.1 Application of multiview 3D to multitemporal data
296(8)
9.2.2 Application of multiview 3D to multistats data
304(1)
9.2.3 Application of multiview 3D to a combination of multistatic and multitemporal data
305(3)
9.3 Conclusion
308(3)
References
308(3)
10 Challenges of semi-cooperative bi/multistatic synthetic aperture radar (SAR) using Cosmo-SkyMed as an illuminator
311(14)
Claire Stevenson
Matthew Nottingham
Darren Muff
David Blacknell
10.1 Introduction
311(1)
10.2 Semi-cooperative bi/multistatic data collection
312(1)
10.3 Hardware considerations
313(2)
10.3.1 Analogue-to-digital convenors (ADCs): bulk delay offset
313(1)
10.3.2 Clock drift
314(1)
10.4 Semi-cooperative bistatic SAR signal processing
315(2)
10.5 Effect of errors in transmitter position
317(2)
10.6 Image formation
319(2)
10.7 Experimental validation of semi-cooperative bistatic SAR signal processing
321(1)
10.8 Conclusions
322(3)
References
323(2)
11 Lesson learnt from NATO SET-196 RTG on `multichannel/multi-static radar imaging of non-cooperative targets'
325(8)
Marco Martorella
David Greig
11.1 The role and impact of NATO SET-196 RTG within and outside NATO
325(1)
11.2 Progress made by NATO SET-196 within multidimensional radar imaging
326(2)
11.3 Lesson learnt and open issues
328(1)
11.4 The way ahead for multidimensional radar imaging systems
328(5)
11.4.1 Towards affordable multidimensional radar imaging systems
329(1)
11.4.2 Clusters and swarms
330(3)
Index 333
Marco Martorella is an Associate Professor at the Department of Information Engineering of the University of Pisa, Italy, and Director of the Radar and Surveillance Systems National Laboratory of the Italian Inter-University Consortium for Telecommunications (CNIT). He is author of about 200 international journal and conference papers, thirteen book chapters, and two books on radar technologies. He is currently a member of the IEEE Radar Systems Panel, a member of the NATO Sensors and Electronics Technology Panel and has chaired four NATO Research Task Groups since 2012. His research interests are mainly in the field of radar imaging and multichannel radar signal processing.
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