Journal of IKEEE (전기전자학회논문지)

Institute of Korean Electrical and Electronics Engineers (한국전기전자학회)
권호 표지
DOI QR코드

DOI QR Code

Improve object recognition using UWB SAR imaging with compressed sensing

  • Pham, The Hien (Dept. of Information & Communication Eng., Kongju National University) ;
  • Hong, Ic-Pyo (Dept. of Information & Communication Eng., Kongju National University)
  • Received : 2021.02.16
  • Accepted : 2021.03.23
  • Published : 2021.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, the compressed sensing basic pursuit denoise algorithm adopted to synthetic aperture radar imaging is investigated to improve the object recognition. From the incomplete data sets for image processing, the compressed sensing algorithm had been integrated to recover the data before the conventional back- projection algorithm was involved to obtain the synthetic aperture radar images. This method can lead to the reduction of measurement events while scanning the objects. An ultra-wideband radar scheme using a stripmap synthetic aperture radar algorithm was utilized to detect objects hidden behind the box. The Ultra-Wideband radar system with 3.1~4.8 GHz broadband and UWB antenna were implemented to transmit and receive signal data of two conductive cylinders located inside the paper box. The results confirmed that the images can be reconstructed by using a 30% randomly selected dataset without noticeable distortion compared to the images generated by full data using the conventional back-projection algorithm.

Keywords

References

  1. S. H. Chang, N. Mitsumoto and J.W. Burdick, "An algorithm for UWB radar-based human detection," IEEE National Radar Conference Proceedings, pp.1-6, 2009. DOI: 10.1109/RADAR.2009.4976999
  2. J. D. Taylor, "Ultrawideband Radar: Applications and Design," CRC Press, 2012.
  3. Kram E Khuda, "UWB Technology and its Applications," Dusan Kocur, IntechOpen, 2019.
  4. Xia Z, Zhang Q, Ye S, Wang Y, Chen C, Yin H, "A novel handheld pseudo random coded UWB radar for human sensing applications," IEICE Electron Express, vol.11, no.23. pp.1-7, 2014. DOI: 10.1587/elex.11.20140981
  5. Xu Y, Wu S, Shao J, Chen J, Fang G "Life detection and location by MIMO ultra wideband radar," 14th International Conference on Ground Penetrating Radar (GPR), pp.80-84, 2012. DOI: 10.1109/ICGPR.2012.6254837
  6. Nguyen Trung Kien, Ic-pyo Hong, "Evaluation of Common Building Wall in See Through Wall Application of Ultra wideband Synthetic Aperture Radar," Journal of Electrical Engineering and Technology, vol.16, pp.437-442, 2020. DOI: 10.1007/s42835-020-00540-4
  7. L. H. Nguyen, M. Ressler and J. Sichina, "Sensing through the wall imaging using the Army Research Lab ultra-wideband synchronous impulse reconstruction (UWB SIRE) radar," Proc. SPIE, vol.6947, 2008. DOI: 10.1117/12.776869
  8. D. L. Donoho, "Compressed sensing," IEEE Transactions on Information Theory, vol.52, no., pp.1289-1306, 2006. DOI: 10.1109/TIT.2006.871582
  9. E. J. Candès, J. K. Romberg, and T. Tao, "Stable signal recovery from incomplete and inaccurate measurements," Communications on pure and applied mathematics, vol.59, no.8, pp.1207-1223, 2006. DOI: 10.1002/cpa.20124
  10. Xuehui Zhang, Xiaoli Xi, Yurong Pu, Chen Zheng, Daocheng Wu, "Compressive sensing for through-wall imaging with spread spectrum radar," 2016 IEEE MTT-S International Wireless Symposium (IWS), pp.1-4, 2016. DOI: 10.1109/IEEE-IWS.2016.7585489
  11. Peyman Kazemi, Mahmood Modarres-Hashemi, Mohammad Mahdi Naghsh, "Block compressive sensing in Synthetic Aperture Radar (SAR)," 2016 24th Iranian Conference on Electrical Engineering (ICEE), pp.1324-1329. 2016. DOI: 10.1109/IranianCEE.2016.7585726
  12. Stephen L.Bruton and J,Nathan Kutz, "Data driven science & Engineering," Cambridge University Press, 2017.
  13. Humatics UWB Radios, "Humatics Radar Kit Quick Start Guide," Humatics® Corporation, 2020.
  14. Ian G. Cumming, Frank H. Wong, "Digital processing of Synthetic Aperture Radar data: algorithms and implementation," Artech House, 2005.
  15. E. van den Berg and M. P. Friedlander, "SPGL1: A solver for large-scale sparse reconstruction," https://www.cs.ubc.ca/-mpf/ spgl1/index.html.
  16. Armin W. Doerry, Edward E. Bishop, John A. Miller, "Basics of backprojection algorithm for processing synthetic aperture radar images," Sandia National Laboratories, 2016.