2021-01-08
Hardware Enabled Acceleration of Near-Field Coded Aperture Radar Physical Model for Millimetre-Wave Computational Imaging
By
Progress In Electromagnetics Research B, Vol. 90, 91-108, 2021
Abstract
There is an increasing demand in real-time imagery applications such as rapid response to disaster rescue and security screening to name a few. The throughput of a radar imaging system is mainly controlled by two parameters; data acquisition time and signal processing time. To minimize the data acquisition time, various methods are being tried and tested by researchers worldwide. Among them is the computational imaging (CI) technique, which relies on using coded apertures to encode the radar back-scattered measurements onto a set of spatio-temporarily incoherent radiation patterns. Such a CI-based imaging approach eliminates the requirement for a raster scan and can substantially simplify the physical hardware architecture. Equally important is the processing time needed to retrieve the scene information from the coded back-scattered measurements. In CI, the simplification in the hardware layer comes at the cost of increased complexity in the signal processing layer due to the indirect mapping and compression of the scene information through the spatio-temporally incoherent transfer function of the coded apertures. To address this particular challenge, this paper presents a hardware-based solution for CI signal processing using a Field Programmable Gate Array (an Xilinx Virtex-7 (XC7VX485T) FPGA chip) architecture. In particular, the proposed method consists of calculating the CI sensing matrix using the FPGA chip and storing it on the FPGA platform for image reconstruction. For the adjoint operation, the calculated sensing matrix is applied on the measured back-scattered waves from the target object. We demonstrate that the FPGA based calculation can reach 21.9 times faster speed than conventional brute-force solutions.
Citation
Rahul Sharma, Okan Yurduseven, Bhabesh Deka, and Vincent Fusco, "Hardware Enabled Acceleration of Near-Field Coded Aperture Radar Physical Model for Millimetre-Wave Computational Imaging," Progress In Electromagnetics Research B, Vol. 90, 91-108, 2021.
doi:10.2528/PIERB20112305
References

1. Ahmed, S. S., A. Genghammer, A. Schiessl, and L.-P. Schmidt, "Fully electronic e-band personnel imager of 2m2 aperture based on a multistatic architecture," IEEE Transactions on Microwave Theory and Techniques, Vol. 61, No. 1, 651-657, 2012.
doi:10.1109/TMTT.2012.2228221        Google Scholar

2. Yurduseven, O., "Indirect microwave holographic imaging of concealed ordnance for airport security imaging systems," Progress In Electromagnetics Research, Vol. 146, 7-13, 2014.
doi:10.2528/PIER14032304        Google Scholar

3. Wang, Z., T. Chang, and H. Cui, "Review of active millimeter wave imaging techniques for personnel security screening," IEEE Access, Vol. 7, 148336-148350, 2019.
doi:10.1109/ACCESS.2019.2946736        Google Scholar

4. Yurduseven, O., T. Fromenteze, C. Decroze, and V. Fusco, "Frequency-diverse computational automotive radar technique for debris detection," IEEE Sensors Journal, Vol. 20, No. 22, 13167-13177, 2020.
doi:10.1109/JSEN.2020.3004065        Google Scholar

5. Castro, J., S. Singh, A. Arora, S. Louie, and D. Senic, "Enabling safe autonomous vehicles by advanced mm-wave radar simulations," IEEE MTT-S International Microwave Symposium Digest, Vol. 2019-June, 1476-1479, 2019.        Google Scholar

6. Diebold, A., M. Imani, and D. Smith, "Phaseless radar coincidence imaging with a MIMO SAR platform," Remote Sensing, Vol. 11, No. 5, 2019.
doi:10.3390/rs11050533        Google Scholar

7. Sarabandi, K., M. Vahidpour, M. Moallem, and J. East, "Compact beam scanning 240 GHz radar for navigation and collision avoidance," Proceedings of SPIE — The International Society for Optical Engineering, Vol. 8031, 2011.        Google Scholar

8. Detlefsen, J., "Industrial applications of microwave imaging," 1991 21st European Microwave Conference, Vol. 1, 108-119, 1991.
doi:10.1109/EUMA.1991.336423        Google Scholar

9. Bilik, I., O. Longman, S. Villeval, and J. Tabrikian, "The rise of radar for autonomous vehicles: Signal processing solutions and future research directions," IEEE Signal Processing Magazine, Vol. 36, No. 5, 20-31, 2019.
doi:10.1109/MSP.2019.2926573        Google Scholar

10. Shehab, S., J. Feng, and N. Karmakar, "Trends on remote sensing technology: Receiver architectures and antenna systems," 1st International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2019, 227-232, 2019.
doi:10.1109/ICREST.2019.8644361        Google Scholar

11. Li, Q., K. Chen, W. Guo, L. Lang, F. He, L. Chen, and Z. Xiong, "An aperture synthesis radiometer at millimeter wave band," 2008 International Conference on Microwave and Millimeter Wave Technology Proceedings, ICMMT, Vol. 4, 1699-1701, 2008.        Google Scholar

12. Piddyachiy, V., V. Shulga, V. Myshenko, A. Korolev, A. Myshenko, and A. Antyufeyev, "Ground-based 3 mm-wave radiometer for spectroscopic observations of atmospheric ozone and carbon monoxide," 2010 International Kharkov Symposium on Physics and Engineering of Microwaves, Millimeter and Submillimeter Waves, MSMW’2010, 2010.        Google Scholar

13. Sheen, D. M., D. L. McMakin, and T. E. Hall, "Three-dimensional millimeter-wave imaging for concealed weapon detection," IEEE Transactions on Microwave Theory and Techniques, Vol. 49, No. 9, 1581-1592, 2001.
doi:10.1109/22.942570        Google Scholar

14. Martınez-Lorenzo, J., F. Quivira, and C. Rappaport, "SAR imaging of suicide bombers wearing concealed explosive threats," Progress In Electromagnetics Research, Vol. 125, 255-272, 2012.
doi:10.2528/PIER11120518        Google Scholar

15. Demirci, S., H. Cetinkaya, E. Yigit, C. Ozdemir, and A. Vertiy, "A study on millimeter-wave imaging of concealed objects: Application using backprojection algorithm," Progress In Electromagnetics Research, Vol. 128, 457-477, 2012.
doi:10.2528/PIER12050210        Google Scholar

16. Hansen, H., A. Kulessa, and G. Brooker, "Millimetre-wave radars in targeting and data linking operations," 2003 Proceedings of the International Conference on Radar, RADAR 2003, 230-234, 2003.
doi:10.1109/RADAR.2003.1278744        Google Scholar

17. Fromenteze, T., O. Yurduseven, M. F. Imani, J. Gollub, C. Decroze, D. Carsenat, and D. R. Smith, "Computational imaging using a mode-mixing cavity at microwave frequencies," Applied Physics Letters, Vol. 106, No. 19, 2015.
doi:10.1063/1.4921081        Google Scholar

18. Qi, F., I. Ocket, D. Schreurs, and B. Nauwelaers, "A system-level simulator for indoor mmW SAR imaging and its applications," Optics Express, Vol. 20, No. 21, 23811-23820, 2012.
doi:10.1364/OE.20.023811        Google Scholar

19. Laviada, J., A. Arboleya-Arboleya, Y. Alvarez-Lopez, C. Garcia-Gonzalez, and F. Las-Heras, "Phaseless synthetic aperture radar with efficient sampling for broadband near-field imaging: Theory and validation," IEEE Transactions on Antennas and Propagation, Vol. 63, No. 2, 573-584, 2015.
doi:10.1109/TAP.2014.2378262        Google Scholar

20. Charvat, G., L. Kempel, E. Rothwell, C. Coleman, and E. Mokole, "An Ultrawideband (UWB) switched-antenna-array radar imaging system," IEEE International Symposium on Phased Array Systems and Technology, 543-550, 2010.
doi:10.1109/ARRAY.2010.5613313        Google Scholar

21. Withington, S., G. Saklatvala, and M. Hobson, "Partially coherent analysis of imaging and interferometric phased arrays: Noise, correlations, and uctuations," Journal of the Optical Society of America A: Optics and Image Science, and Vision, Vol. 23, No. 6, 1340-1348, 2006.
doi:10.1364/JOSAA.23.001340        Google Scholar

22. Gollub, J., O. Yurduseven, K. Trofatter, D. Arnitz, F. Imani, T. Sleasman, M. Boyarsky, A. Rose, A. Pedross-Engel, H. Odabasi, M. Reynolds, and D. Smith, "Large metasurface aperture for millimeter wave computational imaging at the human-scale," Scientific Reports, Vol. 7, 2017.        Google Scholar

23. Molaei, A., J. Heredia-Juesas, G. Ghazi, J. Vlahakis, and J. A. Martinez-Lorenzo, "Digitized metamaterial absorber-based compressive reflector antenna for high sensing capacity imaging," IEEE Access, Vol. 7, 1160-1173, 2019.
doi:10.1109/ACCESS.2018.2881103        Google Scholar

24. Barbastathis, G., A. Ozcan, and G. Situ, "On the use of deep learning for computational imaging," Optica, Vol. 6, No. 8, 921-943, 2019.
doi:10.1364/OPTICA.6.000921        Google Scholar

25. Fromenteze, T., E. L. Kpre, D. Carsenat, C. Decroze, and T. Sakamoto, "Single-shot compressive multiple-inputs multiple-outputs radar imaging using a two-port passive device," IEEE Access, Vol. 4, 1050-1060, 2016.
doi:10.1109/ACCESS.2016.2543525        Google Scholar

26. Hunt, J., T. Driscoll, A. Mrozack, G. Lipworth, M. Reynolds, D. Brady, and D. R. Smith, "Metamaterial apertures for computational imaging," Science, Vol. 339, No. 6117, 310-313, 2013.
doi:10.1126/science.1230054        Google Scholar

27. Yurduseven, O., V. R. Gowda, J. N. Gollub, and D. R. Smith, "Printed aperiodic cavity for computational and microwave imaging," IEEE Microwave and Wireless Components Letters, Vol. 26, No. 5, 367-369, 2016.
doi:10.1109/LMWC.2016.2548443        Google Scholar

28. Yurduseven, O., J. Gollub, A. Rose, D.Marks, and D. Smith, "Design and simulation of a frequency-diverse aperture for imaging of human-scale targets," IEEE Access, Vol. 4, 5436-5451, 2016.
doi:10.1109/ACCESS.2016.2604823        Google Scholar

29. Chi, W. and N. George, "Phase-coded aperture for optical imaging," Optics Communications, Vol. 282, 2110-2117, June 2008.        Google Scholar

30. Don, M. L., C. Fu, and G. R. Arce, "Compressive imaging via a rotating coded aperture," Applied Optics, Vol. 56, No. 3, B142, 2017.
doi:10.1364/AO.56.00B142        Google Scholar

31. Watts, C. M., D. Shrekenhamer, J. Montoya, G. Lipworth, J. Hunt, T. Sleasman, S. Krishna, D. R. Smith, and W. J. Padilla, "Terahertz compressive imaging with metamaterial spatial light modulators," Nature Photonics, Vol. 8, No. 8, 605, 2014.
doi:10.1038/nphoton.2014.139        Google Scholar

32. Sleasman, T., M. F. Imani, J. N. Gollub, and D. R. Smith, "Dynamic metamaterial aperture for microwave imaging," Applied Physics Letters, Vol. 107, No. 20, 204104, 2015.
doi:10.1063/1.4935941        Google Scholar

33. Imani, M., J. Gollub, O. Yurduseven, A. Diebold, M. Boyarsky, T. Fromenteze, L. Pulido-Mancera, T. Sleasman, and D. Smith, "Review of metasurface antennas for computational microwave imaging," IEEE Transactions on Antennas and Propagation, Vol. 68, No. 3, 1860-1875, 2020.
doi:10.1109/TAP.2020.2968795        Google Scholar

34. Andrecut, M., "Fast GPU implementation of sparse signal recovery from random projections," Engineering Letters, Vol. 17, No. 3, 2009.        Google Scholar

35. Zhou, B., Y. Peng, C. Yeh, and J. Tang, "GPGPU accelerated fast convolution back-projection for radar image reconstruction," Tsinghua Science and Technology, Vol. 16, No. 3, 256-263, 2011.
doi:10.1016/S1007-0214(11)70037-2        Google Scholar

36. Park, S. and D. Shires, "CUDA optimization techniques for SAR imaging algorithm," Proceedings of the 2010 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2010, Vol. 1, 36-40, 2010.        Google Scholar

37. Clemente, C., M. Di Bisceglie, M. Di Santo, N. Ranaldo, and M. Spinelli, "Processing of synthetic aperture radar data with GPGPU," IEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation, 309-314, 2009.        Google Scholar

38. Rybalkin, V. and N. Wehn, "When massive GPU parallelism Ain’t enough: A novel hardware architecture of 2D-LSTM neural network," FPGA 2020 — 2020 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, 111-121, 2020.
doi:10.1145/3373087.3375301        Google Scholar

39. Farhadi, M., M. Ghasemi, and Y. Yang, "A novel design of adaptive and hierarchical convolutional neural networks using partial reconfiguration on FPGA," 2019 IEEE High Performance Extreme Computing Conference, HPEC 2019, 2019.        Google Scholar

40. Zhou, X., Z. Yu, Y. Cao, and S. Jiang, "SAR imaging realization with FPGA based on VIVADO HLS," ICSIDP 2019 — IEEE International Conference on Signal, Information and Data Processing 2019, 2019.        Google Scholar

41. Liu, R., D. Zhu, D. Wang, and W. Du, "High resolution SAR signal processing system using FPGA," 2019 International Applied Computational Electromagnetics Society Symposium-China, ACES 2019, 2019.        Google Scholar

42. Di, W., C. Chen, and Y. Liu, "FPGA-based parallel system for synthetic aperture radar imaging," 2018 International Conference on Electronics Technology, ICET 2018, 430-433, 2018.        Google Scholar

43. Yurduseven, O., M. A. B. Abbasi, T. Fromenteze, and V. Fusco, "Lens-loaded coded aperture with increased information capacity for computational microwave imaging," Remote Sensing, Vol. 12, No. 9, 1531, 2020.
doi:10.3390/rs12091531        Google Scholar

44. Sleasman, T., M. Boyarsky, M. F. Imani, T. Fromenteze, J. N. Gollub, and D. R. Smith, "Single-frequency microwave imaging with dynamic metasurface apertures," JOSA B, Vol. 34, No. 8, 1713-1726, 2017.
doi:10.1364/JOSAB.34.001713        Google Scholar

45. Yurduseven, O., M. Imani, H. Odabasi, J. Gollub, G. Lipworth, A. Rose, and D. Smith, "Resolution of the frequency diverse metamaterial aperture imager," Progress In Electromagnetics Research, Vol. 150, 97-107, 2015.
doi:10.2528/PIER14113002        Google Scholar

46. Peng, R., O. Yurduseven, T. Fromenteze, and D. R. Smith, "Advanced processing of 3D computational microwave polarimetry using a near-field frequency-diverse antenna," IEEE Access, Vol. 8, 166261-166272, 2020.
doi:10.1109/ACCESS.2020.3021418        Google Scholar

47. Fromenteze, T., O. Yurduseven, M. Boyarsky, J. Gollub, D. L. Marks, and D. R. Smith, "Computational polarimetric microwave imaging," Optics Express, Vol. 25, No. 22, 27488-27505, 2017.
doi:10.1364/OE.25.027488        Google Scholar

48. Lipworth, G., A. Rose, O. Yurduseven, V. R. Gowda, M. F. Imani, H. Odabasi, P. Trofatter, J. Gollub, and D. R. Smith, "Comprehensive simulation platform for a metamaterial imaging system," Applied Optics, Vol. 54, No. 31, 9343-9353, 2015.
doi:10.1364/AO.54.009343        Google Scholar

49. Mandel, L. and E. Wolf, Optical Coherence and Quantum Optics, Cambridge University Press, 1995.
doi:10.1017/CBO9781139644105

50. Hecht, K. T., The Born Approximation, 462-476, Springer New York, 2000.

51. Rashidi-Ranjbar, E. and M. Dehmollaian, "Microwave imaging using frequency-diverse scattering of a random rough surface," ICEE 2019 — 27th Iranian Conference on Electrical Engineering, 1679-1681, 2019.        Google Scholar

52. Venkatesh, S., N. Viswanathan, and D. Schurig, "W-band sparse synthetic aperture for computational imaging," Optics Express, Vol. 24, No. 8, 8317-8331, 2016.
doi:10.1364/OE.24.008317        Google Scholar

53. Kowdle, A., C. Rhemann, S. Fanello, A. Tagliasacchi, J. Taylor, P. Davidson, M. Dou, K. Guo, C. Keskin, S. Khamis, V. Tankovich, and J. Valentin, "The need 4 speed in real-time dense visual tracking," ACM Transactions on Graphics, Vol. 37, No. 6, 2018.        Google Scholar

54. Malczewski, K., "Rapid diffusion weighted imaging with enhanced resolution," Applied Magnetic Resonance, Vol. 51, No. 3, 221-239, 2020.
doi:10.1007/s00723-019-01185-x        Google Scholar

55. X. Inc. Virtex-7 FPGA Design Summary, p. 5, Xilinx, February 27, 2018.

56. X. Inc. CORDIC v6.0 LogiCORE IP Product Guide, Xilinx, December 20, 2017.

57. X. Inc. 7 Series DSP48E1 Slice User Guide, Xilinx, March 27, 2018.

58. Andraka, R., "A survey of CORDIC algorithms for FPGA based computers," Tech. Rep., 1998.        Google Scholar

59. Yurduseven, O., T. Fromenteze, and D. R. Smith, "Relaxation of alignment errors and phase calibration in computational frequency-diverse imaging using phase retrieval," IEEE Access, Vol. 6, 14884-14894, 2018.
doi:10.1109/ACCESS.2018.2816341        Google Scholar

60. Yurduseven, O., J. N. Gollub, K. P. Trofatter, D. L. Marks, A. Rose, and D. R. Smith, "Software calibration of a frequency-diverse, multistatic, computational imaging system," IEEE Access, Vol. 4, 2488-2497, 2016.
doi:10.1109/ACCESS.2016.2570678        Google Scholar

61. Sleasman, T., M. F. Imani, O. Yurduseven, K. P. Trofatter, V. R. Gowda, D. L. Marks, J. N. Gollub, and D. R. Smith, "Near field scan alignment procedure for electrically large apertures," IEEE Transactions on Antennas and Propagation, Vol. 65, No. 6, 3257-3262, 2017.
doi:10.1109/TAP.2017.2691465        Google Scholar