Real-Time Synthetic Aperture Radar Imaging with Random Sampling Employing Scattered Power Mapping
Abstract
1. Introduction
2. Methodology
2.1. 3D Scanning
2.2. Image Reconstruction with Randomly Sampled Data Employing Scattered Power Mapping
2.3. Image-Convergence Check
3. Validation Examples with Simulated Data
3.1. C Shape Image Reconstruction with Simulated Data
3.2. F Shape Image Reconstruction with LFM Radar Synthetic Data
4. Validation Examples with Measured Data
4.1. Compressed Breast Phantom Imaging
4.2. Imaging of Various Small Items with mm-Wave LFM Radar
5. Discussion
6. Conclusions and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Correction Statement
References
- Kharkovsky, S.; Case, J.; Abou-Khousa, M.; Zoughi, R.; Hepburn, F. Millimeter-wave detection of localized anomalies in the space shuttle external fuel tank insulating foam. IEEE Trans. Instrum. Meas. 2006, 55, 1250–1257. [Google Scholar] [CrossRef]
- Dvorsky, M.; Sim, S.Y.; Motes, D.T.; Watt, T.; Shah, A.; Qaseer, M.T.A.; Zoughi, R. Multistatic Ka-Band (26.5–40 GHz) Millimeter-Wave 3-D Imaging System. IEEE Trans. Instrum. Meas. 2023, 72, 1–14. [Google Scholar] [CrossRef]
- Horst, M.J.; Ghasr, M.T.; Zoughi, R. A Compact Microwave Camera Based on Chaotic Excitation Synthetic-Aperture Radar. IEEE Trans. Antennas Propag. 2019, 67, 4148–4161. [Google Scholar] [CrossRef]
- Truong, T.; Dinh, A.; Wahid, K. An Ultra-Wideband Frequency System for Non-Destructive Root Imaging. Sensors 2018, 18, 2438. [Google Scholar] [CrossRef]
- Abou-Khousa, M.A.; Rahman, M.S.U.; Donnell, K.M.; Qaseer, M.T.A. Detection of Surface Cracks in Metals Using Microwave and Millimeter-Wave Nondestructive Testing Techniques—A Review. IEEE Trans. Instrum. Meas. 2023, 72, 1–18. [Google Scholar] [CrossRef]
- Liu, C.; Zoughi, R. Adaptive Synthetic Aperture Radar (SAR) Imaging for Optimal Cross-Range Resolution and Image Quality in NDE Applications. IEEE Trans. Instrum. Meas. 2021, 70, 1–7. [Google Scholar] [CrossRef]
- Paun, M. Through-Wall Imaging Using Low-Cost Frequency-Modulated Continuous Wave Radar Sensors. Remote Sens. 2024, 16, 1426. [Google Scholar] [CrossRef]
- Zhuravlev, A.; Razevig, V.; Rogozin, A.; Chizh, M. Microwave Imaging of Concealed Objects with Linear Antenna Array and Optical Tracking of the Target for High-Performance Security Screening Systems. IEEE Trans. Microw. Theory Tech. 2023, 71, 1326–1336. [Google Scholar] [CrossRef]
- Gui, S.; Li, J.; Yang, Y.; Zuo, F.; Pi, Y. A SAR Imaging Method for Walking Human Based on mωka-FrFT-mmGLRT. IEEE Trans. Geosci. Remote Sens. 2022, 60, 1–12. [Google Scholar] [CrossRef]
- Gui, S.; Yang, Y.; Li, J.; Zuo, F.; Pi, Y. THz Radar Security Screening Method for Walking Human Torso with Multi-Angle Synthetic Aperture. IEEE Sensors J. 2021, 21, 17962–17972. [Google Scholar] [CrossRef]
- Zhuravlev, A.; Razevig, V.; Chizh, M.; Dong, G.; Hu, B. A New Method for Obtaining Radar Images of Concealed Objects in Microwave Personnel Screening Systems. IEEE Trans. Microw. Theory Tech. 2021, 69, 357–364. [Google Scholar] [CrossRef]
- Chen, X.; Yang, Q.; Wang, H.; Zeng, Y.; Deng, B. Adaptive ADMM-Based High-Quality Fast Imaging Algorithm for Short-Range MMW MIMO-SAR Systems. IEEE Trans. Antennas Propag. 2023, 71, 8925–8935. [Google Scholar] [CrossRef]
- Pang, L.; Liu, H.; Chen, Y.; Miao, J. Real-time Concealed Object Detection from Passive Millimeter Wave Images Based on the YOLOv3 Algorithm. Sensors 2020, 20, 1678. [Google Scholar] [CrossRef]
- Liang, F.; Wang, P.; Lv, H.; Bai, M.; An, Q.; Han, S.; Zhang, Y.; Wang, J. Change Detection and Enhanced Imaging of Vital Signs Based on Arc-Scanning SAR. IEEE Sensors J. 2024, 24, 8304–8313. [Google Scholar] [CrossRef]
- Owda, A.Y.; Owda, M.; Rezgui, N.D. Synthetic Aperture Radar Imaging for Burn Wounds Diagnostics. Sensors 2020, 20, 847. [Google Scholar] [CrossRef]
- Klemm, M.; Leendertz, J.A.; Gibbins, D.; Craddock, I.J.; Preece, A.; Benjamin, R. Microwave Radar-Based Differential Breast Cancer Imaging: Imaging in Homogeneous Breast Phantoms and Low Contrast Scenarios. IEEE Trans. Antennas Propag. 2010, 58, 2337–2344. [Google Scholar] [CrossRef]
- Li, H.; Zhang, H.; Kong, Y.; Zhou, C. Flexible Dual-Polarized UWB Antenna Sensors for Breast Tumor Detection. IEEE Sens. J. 2022, 22, 13648–13658. [Google Scholar] [CrossRef]
- Ghamati, M.; Taherzadeh, M.; Nabki, F.; Popović, M. Integrated Fast UWB Time-Domain Microwave Breast Screening. IEEE Trans. Instrum. Meas. 2023, 72, 1–12. [Google Scholar] [CrossRef]
- Dachena, C.; Fedeli, A.; Fanti, A.; Lodi, M.B.; Fumera, G.; Randazzo, A.; Pastorino, M. Microwave Imaging of the Neck by Means of Artificial Neural Networks for Tumor Detection. IEEE Open J. Antennas Propag. 2021, 2, 1044–1056. [Google Scholar] [CrossRef]
- Hosseinzadegan, S.; Fhager, A.; Persson, M.; Geimer, S.D.; Meaney, P.M. Discrete Dipole Approximation-Based Microwave Tomography for Fast Breast Cancer Imaging. IEEE Trans. Microw. Theory Tech. 2021, 69, 2741–2752. [Google Scholar] [CrossRef]
- Zhang, L.; Qiao, Z.; Xing, M.d.; Yang, L.; Bao, Z. A Robust Motion Compensation Approach for UAV SAR Imagery. IEEE Trans. Geosci. Remote Sens. 2012, 50, 3202–3218. [Google Scholar] [CrossRef]
- Zhang, F.; Yan, S.; Fu, Y.; Yang, W.; Zhang, W.; Yu, R. A Novel Motion Compensation Framework for Micro UAV FMCW SAR. In Proceedings of the 2023 IEEE 16th International Conference on Electronic Measurement & Instruments (ICEMI), Harbin, China, 9–11 August 2023; pp. 304–308. [Google Scholar] [CrossRef]
- Koo, V.; Chan, Y.K.; Vetharatnam, G.; Chua, M.Y.; Lim, C.H.; Lim, C.S.; Thum, C.C.; Lim, T.S.; bin Ahmad, Z.; Mahmood, K.A.; et al. A new unmanned aerial vehicle synthetic aperture radar for environmental monitoring. Prog. Electromagn. Res. 2012, 122, 245–268. [Google Scholar] [CrossRef]
- Ding, J.; Zhang, K.; Huang, X.; Xu, Z. High Frame-Rate Imaging Using Swarm of UAV-Borne Radars. IEEE Trans. Geosci. Remote Sens. 2024, 62, 1–12. [Google Scholar] [CrossRef]
- Faul, F.T.; Korthauer, D.; Eibert, T.F. Impact of Rotor Blade Rotation of UAVs on Electromagnetic Field Measurements. IEEE Trans. Instrum. Meas. 2021, 70, 1–9. [Google Scholar] [CrossRef]
- Lyu, M.; Zhao, Y.; Huang, C.; Huang, H. Unmanned Aerial Vehicles for Search and Rescue: A Survey. Remote Sens. 2023, 15, 3266. [Google Scholar] [CrossRef]
- Garcia Fernandez, M.; Alvarez Lopez, Y.; Arboleya Arboleya, A.; Gonzalez Valdes, B.; Rodriguez Vaqueiro, Y.; Las-Heras Andres, F.; Pino Garcia, A. Synthetic Aperture Radar Imaging System for Landmine Detection Using a Ground Penetrating Radar on Board a Unmanned Aerial Vehicle. IEEE Access 2018, 6, 45100–45112. [Google Scholar] [CrossRef]
- Lopez, Y.A.; Garcia-Fernandez, M.; Alvarez-Narciandi, G.; Andres, F.L.H. Unmanned Aerial Vehicle-Based Ground-Penetrating Radar Systems: A review. IEEE Geosci. Remote Sens. Mag. 2022, 10, 66–86. [Google Scholar] [CrossRef]
- Garcia-Fernandez, M.; Alvarez-Narciandi, G.; Heras, F.L.; Alvarez-Lopez, Y. Comparison of Scanning Strategies in UAV-Mounted Multichannel GPR-SAR Systems Using Antenna Arrays. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2024, 7, 3571–3586. [Google Scholar] [CrossRef]
- Amiri, A.; Tong, K.; Chetty, K. Feasibility study of multi-frequency Ground Penetrating Radar for rotary UAV platforms. In Proceedings of the IET International Conference on Radar Systems (Radar 2012), Glasgow, UK, 22–25 October 2012; pp. 1–6. [Google Scholar] [CrossRef]
- Weib, M.; Ender, J. A 3D imaging radar for small unmanned airplanes—ARTINO. In Proceedings of the 2005 European Radar Conference (EURAD 2005), Paris, France, 3–4 October 2005; pp. 209–212. [Google Scholar] [CrossRef]
- Yarleque, M.A.; Alvarez, S.; Martinez, H.J. FMCW GPR radar mounted in a mini-UAV for archaeological applications: First analytical and measurement results. In Proceedings of the 2017 International Conference on Electromagnetics in Advanced Applications (ICEAA), Verona, Italy, 11–15 September 2017; pp. 1646–1648. [Google Scholar] [CrossRef]
- Ludeno, G.; Catapano, I.; Renga, A.; Vetrella, A.R.; Fasano, G.; Soldovieri, F. Assessment of a micro-UAV system for microwave tomography radar imaging. Remote Sens. Environ. 2018, 212, 90–102. [Google Scholar] [CrossRef]
- Grathwohl, A.; Arendt, B.; Grebner, T.; Waldschmidt, C. Detection of Objects Below Uneven Surfaces with a UAV-Based GPSAR. IEEE Trans. Geosci. Remote Sens. 2023, 61, 1–13. [Google Scholar] [CrossRef]
- Pierce, A. Walabot DIY can see into walls. Technol. Today 2017, 76, 8–9. [Google Scholar]
- Vayyar Imaging—Home. Available online: https://vayyar.com/ (accessed on 11 June 2024).
- The World’s Most Advanced Stud Finder. Available online: https://walabot.com/ (accessed on 11 June 2024).
- Nikolova, N.K. Introduction to Microwave Imaging; EuMA High Frequency Technologies Series; Cambridge University Press: Cambridge, UK, 2017. [Google Scholar] [CrossRef]
- Amineh, R.K.; Nikolova, N.K.; Ravan, M. Real-Time Three-Dimensional Imaging of Dielectric Bodies Using Microwave/Millimeter Wave Holography; John Wiley & Sons: Hoboken, NJ, USA, 2019. [Google Scholar]
- Sheen, D.; McMakin, D.; Hall, T. Three-dimensional millimeter-wave imaging for concealed weapon detection. IEEE Trans. Microw. Theory Tech. 2001, 49, 1581–1592. [Google Scholar] [CrossRef]
- Austin, C.D.; Ertin, E.; Moses, R.L. Sparse Signal Methods for 3-D Radar Imaging. IEEE J. Sel. Top. Signal Process. 2011, 5, 408–423. [Google Scholar] [CrossRef]
- Fessler, J.; Sutton, B. Nonuniform fast Fourier transforms using min-max interpolation. IEEE Trans. Signal Process. 2003, 51, 560–574. [Google Scholar] [CrossRef]
- Sun, D.; Pang, B.; Xing, S.; Li, Y.; Wang, X. Direct 3-D Sparse Imaging Using Non-Uniform Samples without Data Interpolation. Electronics 2020, 9, 321. [Google Scholar] [CrossRef]
- Case, J.T.; Ghasr, M.T.; Zoughi, R. Optimum 2-D Nonuniform Spatial Sampling for Microwave SAR-Based NDE Imaging Systems. IEEE Trans. Instrum. Meas. 2012, 61, 3072–3083. [Google Scholar] [CrossRef]
- Marvasti, F. Nonuniform sampling theorems for bandpass signals at or below the Nyquist density. IEEE Trans. Signal Process. 1996, 44, 572–576. [Google Scholar] [CrossRef]
- Marvasti, F. Interpolation of lowpass signals at half the Nyquist rate. In Proceedings of the 1995 International Conference on Acoustics, Speech, and Signal Processing, Detroit, MI, USA, 9–12 May 1995; Volume 2, pp. 1225–1228. [Google Scholar] [CrossRef]
- Zhou, S.; Yang, L.; Zhao, L.; Bi, G. Forward Velocity Extraction From UAV Raw SAR Data Based on Adaptive Notch Filtering. IEEE Geosci. Remote Sens. Lett. 2016, 13, 1211–1215. [Google Scholar] [CrossRef]
- Farhadi, M.; Feger, R.; Fink, J.; Wagner, T.; Stelzer, A. Combining MIMO DBF with Automotive Synthetic Aperture Radar Imaging and Phase Error Correction. IEEE Access 2024, 12, 31944–31959. [Google Scholar] [CrossRef]
- Saurer, M.M.; Hofmann, B.; Eibert, T.F. A Fully Polarimetric Multilevel Fast Spectral Domain Algorithm for 3-D Imaging with Irregular Sample Locations. IEEE Trans. Microw. Theory Tech. 2022, 70, 4231–4242. [Google Scholar] [CrossRef]
- Yegulalp, A. Fast backprojection algorithm for synthetic aperture radar. In Proceedings of the 1999 IEEE Radar Conference. Radar into the Next Millennium (Cat. No.99CH36249), Waltham, MA, USA, 22 April 1999; pp. 60–65. [Google Scholar] [CrossRef]
- Moreira, A.; Huang, Y. Airborne SAR processing of highly squinted data using a chirp scaling approach with integrated motion compensation. IEEE Trans. Geosci. Remote Sens. 1994, 32, 1029–1040. [Google Scholar] [CrossRef]
- Zhou, L.; Zhang, X.; Wang, Y.; Li, L.; Pu, L.; Shi, J.; Wei, S. Unambiguous Reconstruction for Multichannel Nonuniform Sampling SAR Signal Based on Image Fusion. IEEE Access 2020, 8, 71558–71571. [Google Scholar] [CrossRef]
- Krieger, G.; Gebert, N.; Moreira, A. Unambiguous SAR signal reconstruction from nonuniform displaced phase center sampling. IEEE Geosci. Remote Sens. Lett. 2004, 1, 260–264. [Google Scholar] [CrossRef]
- Eldar, Y.; Oppenheim, A. Filterbank reconstruction of bandlimited signals from nonuniform and generalized samples. IEEE Trans. Signal Process. 2000, 48, 2864–2875. [Google Scholar] [CrossRef]
- Lopez-Sanchez, J.; Fortuny-Guasch, J. 3-D radar imaging using range migration techniques. IEEE Trans. Antennas Propag. 2000, 48, 728–737. [Google Scholar] [CrossRef]
- Gao, Y.; Ghasr, M.T.; Zoughi, R. Effects of and Compensation for Translational Position Error in Microwave Synthetic Aperture Radar Imaging Systems. IEEE Trans. Instrum. Meas. 2020, 69, 1205–1212. [Google Scholar] [CrossRef]
- Case, J.T.; Ghasr, M.T.; Zoughi, R. Nonuniform Manual Scanning for Rapid Microwave Nondestructive Evaluation Imaging. IEEE Trans. Instrum. Meas. 2013, 62, 1250–1258. [Google Scholar] [CrossRef]
- Meng, D.; Hu, D.; Ding, C. Precise Focusing of Airborne SAR Data with Wide Apertures Large Trajectory Deviations: A Chirp Modulated Back-Projection Approach. IEEE Trans. Geosci. Remote Sens. 2015, 53, 2510–2519. [Google Scholar] [CrossRef]
- Zhang, G.; Li, C.; Wang, Z.; Hu, J.; Zheng, S.; Liu, X.; Fang, G. An Efficient Spectrum Reconstruction Algorithm for Non-Uniformly Sampled Signals and Its Application in Terahertz SAR. Remote Sens. 2023, 15, 4427. [Google Scholar] [CrossRef]
- Wu, S.; Ding, L.; Li, P.; Li, Y.; Chen, L.; Zhu, Y. Millimeter-Wave SAR Sparse Imaging with 2-D Spatially Pseudorandom Spiral-Sampling Pattern. IEEE Trans. Microw. Theory Tech. 2020, 68, 4672–4683. [Google Scholar] [CrossRef]
- Hu, S.; Molaei, A.M.; Yurduseven, O.; Meng, H.; Nilavalan, R.; Gan, L.; Chen, X. Multistatic MIMO Sparse Imaging Based on FFT and Low-Rank Matrix Recovery Techniques. IEEE Trans. Microw. Theory Tech. 2023, 71, 1285–1295. [Google Scholar] [CrossRef]
- Zhang, T.; Li, Y.; Wang, J.; Xing, M.; Guo, L.; Zhang, P. a Modified Range Model and Extended Omega-K Algorithm for High-Speed-High-Squint SAR with Curved Trajectory. IEEE Trans. Geosci. Remote Sens. 2023, 61, 1–15. [Google Scholar] [CrossRef]
- Xu, Z.; Liu, M.; Zhou, G.; Wei, Z.; Zhang, B.; Wu, Y. An Accurate Sparse SAR Imaging Method for Enhancing Region-Based Features Via Nonconvex and TV Regularization. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2021, 14, 350–363. [Google Scholar] [CrossRef]
- Liu, M.; Pan, J.; Zhu, J.; Chen, Z.; Zhang, B.; Wu, Y. A Sparse SAR Imaging Method for Low-Oversampled Staggered Mode via Compound Regularization. Remote Sens. 2024, 16, 1459. [Google Scholar] [CrossRef]
- Ao, D.; Wang, R.; Hu, C.; Li, Y. A Sparse SAR Imaging Method Based on Multiple Measurement Vectors Model. Remote Sens. 2017, 9, 297. [Google Scholar] [CrossRef]
- Wang, Y.; He, Z.; Zhan, X.; Fu, Y.; Zhou, L. Three-Dimensional Sparse SAR Imaging with Generalized Lq Regularization. Remote Sens. 2022, 14, 288. [Google Scholar] [CrossRef]
- Candes, E.J.; Wakin, M.B. An Introduction To Compressive Sampling. IEEE Signal Process. Mag. 2008, 25, 21–30. [Google Scholar] [CrossRef]
- Pham, T.H.; Kim, K.H.; Hong, I.P. A Study on Millimeter Wave SAR Imaging for Non-Destructive Testing of Rebar in Reinforced Concrete. Sensors 2022, 22, 30. [Google Scholar] [CrossRef]
- Pu, W.; Huang, Y.; Wu, J.; Yang, H.; Yang, J. Fast Compressive Sensing-Based SAR Imaging Integrated with Motion Compensation. IEEE Access 2019, 7, 53284–53295. [Google Scholar] [CrossRef]
- Yang, J.; Jin, T.; Xiao, C.; Huang, X. Compressed Sensing Radar Imaging: Fundamentals, Challenges, and Advances. Sensors 2019, 19, 3100. [Google Scholar] [CrossRef]
- Kang, M.S.; Baek, J.M. SAR Image Reconstruction via Incremental Imaging with Compressive Sensing. IEEE Trans. Aerosp. Electron. Syst. 2023, 59, 4450–4463. [Google Scholar] [CrossRef]
- Dong, B.; Li, G.; Zhang, Q. High-Resolution and Wide-Swath Imaging of Spaceborne SAR via Random PRF Variation Constrained by the Coverage Diagram. IEEE Trans. Geosci. Remote Sens. 2022, 60, 1–16. [Google Scholar] [CrossRef]
- Abo-Zahhad, M.M.; Hussein, A.I.; Mohamed, A.M. Compressive sensing algorithms for signal processing applications: A survey. Int. J. Commun. Netw. Syst. Sci. 2015, 8, 197–216. [Google Scholar]
- Bernhardt, S.; Boyer, R.; Marcos, S.; Larzabal, P. Compressed Sensing with Basis Mismatch: Performance Bounds and Sparse-Based Estimator. IEEE Trans. Signal Process. 2016, 64, 3483–3494. [Google Scholar] [CrossRef]
- Tu, S.; McCombe, J.J.; Shumakov, D.S.; Nikolova, N.K. Fast quantitative microwave imaging with resolvent kernel extracted from measurements. Inverse Probl. 2015, 31, 045007. [Google Scholar] [CrossRef]
- Shumakov, D.S.; Nikolova, N.K. Fast Quantitative Microwave Imaging with Scattered-Power Maps. IEEE Trans. Microw. Theory Tech. 2018, 66, 439–449. [Google Scholar] [CrossRef]
- Kazemivala, R.; Tajik, D.; Nikolova, N.K. Simultaneous Use of the Born and Rytov Approximations in Real-Time Imaging with Fourier-Space Scattered Power Mapping. IEEE Trans. Microw. Theory Tech. 2022, 70, 2904–2920. [Google Scholar] [CrossRef]
- Kazemivala, R.; Pitcher, A.D.; Nguyen, J.; Nikolova, N.K. Real-Time Millimeter-wave Imaging with Linear Frequency Modulation Radar and Scattered Power Mapping. IEEE Trans. Microw. Theory Tech. 2024; Early Access. [Google Scholar] [CrossRef]
- Liu, L.; Trehan, A.C.; Nikolova, N.K. Near-field detection at microwave frequencies based on self-adjoint response sensitivity analysis. Inverse Probl. 2010, 26, 105001. [Google Scholar] [CrossRef]
- Liu, C.; Qaseer, M.T.A.; Zoughi, R. Influence of Antenna Pattern on Synthetic Aperture Radar Resolution for NDE Applications. IEEE Trans. Instrum. Meas. 2021, 70, 8000911. [Google Scholar] [CrossRef]
- Amineh, R.K.; McCombe, J.; Nikolova, N.K. Microwave Holographic Imaging Using the Antenna Phaseless Radiation Pattern. IEEE Antennas Wirel. Propag. Lett. 2012, 11, 1529–1532. [Google Scholar] [CrossRef]
- Meng, Y.; Lin, C.; Zang, J.; Qing, A.; Nikolova, N.K. General Theory of Holographic Inversion with Linear Frequency Modulation Radar and its Application to Whole-Body Security Scanning. IEEE Trans. Microw. Theory Tech. 2020, 68, 4694–4705. [Google Scholar] [CrossRef]
- Gao, J.; Deng, B.; Qin, Y.; Wang, H.; Li, X. An Efficient Algorithm for MIMO Cylindrical Millimeter-Wave Holographic 3-D Imaging. IEEE Trans. Microw. Theory Tech. 2018, 66, 5065–5074. [Google Scholar] [CrossRef]
- Tajik, D.; Kazemivala, R.; Nguyen, J.; Nikolova, N.K. Accurate Range Migration for Fast Quantitative Fourier-Based Image Reconstruction with Monostatic Radar. IEEE Trans. Microw. Theory Tech. 2022, 70, 4273–4283. [Google Scholar] [CrossRef]
- Cheng, Q.; Alomainy, A.; Hao, Y. Near-Field Millimeter-Wave Phased Array Imaging with Compressive Sensing. IEEE Access 2017, 5, 18975–18986. [Google Scholar] [CrossRef]
- Beaverstone, A.S.; Shumakov, D.S.; Nikolova, N.K. Frequency-Domain Integral Equations of Scattering for Complex Scalar Responses. IEEE Trans. Microw. Theory Tech. 2017, 65, 1120–1132. [Google Scholar] [CrossRef]
- Pozar, D.M. Microwave Engineering, 4th ed.; John Wiley & Sons: Hoboken, NJ, USA, 2012. [Google Scholar]
- Wang, Y.; Abbosh, A.M.; Henin, B.; Nguyen, P.T. Synthetic Bandwidth Radar for Ultra-Wideband Microwave Imaging Systems. IEEE Trans. Antennas Propag. 2014, 62, 698–705. [Google Scholar] [CrossRef]
- Yi, L.; Kaname, R.; Mizuno, R.; Li, Y.; Fujita, M.; Ito, H.; Nagatsuma, T. Ultra-Wideband Frequency Modulated Continuous Wave Photonic Radar System for Three-Dimensional Terahertz Synthetic Aperture Radar Imaging. J. Light. Technol. 2022, 40, 6719–6728. [Google Scholar] [CrossRef]
- Özdemir, C.; Demirci, Ş.; Yiğit, E.; Yilmaz, B. A Review on Migration Methods in B-Scan Ground Penetrating Radar Imaging. Math. Probl. Eng. 2014, 2014, 280738. [Google Scholar] [CrossRef]
- Amineh, R.K.; Khalatpour, A.; Xu, H.; Baskharoun, Y.; Nikolova, N.K. Three-dimensional near-field microwave holography for tissue imaging. J. Biomed. Imaging 2012, 2012, 5. [Google Scholar] [CrossRef]
- Amineh, R.K.; McCombe, J.J.; Khalatpour, A.; Nikolova, N.K. Microwave Holography Using Point-Spread Functions Measured with Calibration Objects. IEEE Trans. Instrum. Meas. 2015, 64, 403–417. [Google Scholar] [CrossRef]
- Altair Feko, Version 2018; Altair Engineering Inc.: Troy, MI, USA, 2018.
- MATLAB, Version R2022b; The MathWorks Inc.: Natick, MA, USA, 2022.
- Tajik, D.; Kazemivala, R.; Nikolova, N.K. Real-Time Imaging with Simultaneous Use of Born and Rytov Approximations in Quantitative Microwave Holography. IEEE Trans. Microw. Theory Tech. 2022, 70, 1896–1909. [Google Scholar] [CrossRef]
- Balanis, C.K. Antenna Theory Analysis and Design; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 1997. [Google Scholar]
- Shahmirzadi, N.V.; Tyagi, V.; Nguyen, J.; Kazemivala, R.; Nikolova, N.K.; Chen, C.H. Planar Array of UWB Active Slot Antennas for Microwave Imaging of the Breast. IEEE Trans. Antennas Propag. 2023, 71, 2946–2957. [Google Scholar] [CrossRef]
- Tajik, D.; Pitcher, A.D.; Nikolova, N.K. Comparative study of the Rytov and Born approximations in quantitative microwave holography. Prog. Electromagn. Res. B 2017, 79, 1–19. [Google Scholar] [CrossRef]
- Texas Instruments. IWR1443BOOST Evaluation Module mmWave Sensing Solution; Texas Instruments: Dallas, TX, USA, 2020. [Google Scholar]
- Texas Instruments. DCA1000EVM Real-Time Data-Capture Adapter for Radar Sensing Evaluation Module; Texas Instruments: Dallas, TX, USA, 2019. [Google Scholar]
- Wen, M.; Houlihan, J. Application of the non-uniform Fourier transform to non-uniformly sampled Fourier transform spectrometers. Opt. Commun. 2023, 540, 129491. [Google Scholar]
- Pitcher, A.D.; Baard, C.W.; Georgiev, M.S.; Nikolova, N.K. Ultra-wideband equivalent-time sampling receiver: Limitations and performance analysis. IEEE Trans. Instrum. Meas. 2024; submitted. [Google Scholar]
Material (Structure) | ||
---|---|---|
Carbon–rubber sheet (averaged breast tissue) | 9.6 | 3.82 |
Silicone–rubber sheet (averaged skin tissue) | 19.36 | 14 |
Embedding/matching medium | 11.3 | 2.59 |
Tumour simulant | 64.11 | 22.32 |
Fibroglandular tissue simulant | 17.61 | 7.89 |
Scattering probe (PSF) | 43.7 | 0 |
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Kazemivala, R.; Nikolova, N.K. Real-Time Synthetic Aperture Radar Imaging with Random Sampling Employing Scattered Power Mapping. Sensors 2024, 24, 3849. https://doi.org/10.3390/s24123849
Kazemivala R, Nikolova NK. Real-Time Synthetic Aperture Radar Imaging with Random Sampling Employing Scattered Power Mapping. Sensors. 2024; 24(12):3849. https://doi.org/10.3390/s24123849
Chicago/Turabian StyleKazemivala, Romina, and Natalia K. Nikolova. 2024. "Real-Time Synthetic Aperture Radar Imaging with Random Sampling Employing Scattered Power Mapping" Sensors 24, no. 12: 3849. https://doi.org/10.3390/s24123849
APA StyleKazemivala, R., & Nikolova, N. K. (2024). Real-Time Synthetic Aperture Radar Imaging with Random Sampling Employing Scattered Power Mapping. Sensors, 24(12), 3849. https://doi.org/10.3390/s24123849