Performance Comparison of the Target Detection Methods in High Speed Platform Forward Looking SAR (HSP-FLSAR)

Document Type : Research Paper

Authors

1 electrical and computer engineering, malek ashtar university of technology, tehran, iran

2 Department of electrical and electronic engineering- malek ashtar university of technology

3 Electrical and Electronics Engineering Dept., Malek-e-Ashtar University of Technology, Tehran, Iran

Abstract

Sea target detection in the HSP-FLSAR images has not been addressed so far in the literature. In this paper we carry out a comparative evaluation of existing SAR target detection algorithms in the case of monostatic HSP-FLSAR range Doppler images assuming a platform diving trajectory. To do so, various CFAR methods, including CA-CFAR, SOCA-CFAR, GOCA-CFAR, OS-CFAR, VIE-CFAR, and G0 distribution CFAR algorithms, are used to detect a set of point scatterers in simulated images. The performance of methods is compared based on receiver operating characteristic curves. Simulation results show that OS-CFAR has the best probability of detection for a fixed probability of false alarm. This paper can be a starting point to find better target detection methods in HSP-FLSAR images.

Keywords


  1. J. Saeedi, “Feasibility Study and Conceptual Design of Missile-Borne Synthetic Aperture Radar,” IEEE Trans. Syst., Man, Cybern. Syst., vol. 50, no. 3, pp. 1122-1133, March 2020.
  2. P. Guo, S. Tang, L. Zhang, et al., “Improved focusing approach for highly squinted beam steering SAR,” IET Radar Sonar Navig., vol. 10, no. 8, pp.1394-1399, March 2016.
  3. C. Hongmeng, L. Yaobing, M. Heqiang, et al., “Efficient forward-looking imaging via synthetic bandwidth azimuth modulation imaging radar for high-speed platform,” Sig. Process., vol. 138, pp. 63-70, March 2017.
  4. D. J. Crisp, “The state-of-the-art in ship detection in synthetic aperture radar imagery,” DSTO Info. Sci. Lab., May 2004.
  5. K. El-Darymli, P. McGuire, D. Power, et al., “Target detection in synthetic aperture radar imagery: A state-of-the-art survey,” J. Appl. Rem. Sens., vol. 7, no. 1, p. 71598, May 2013.
  6. B. Magaz, A. Beloucherani, M. Hamadouche, “Automatic threshold selection in OS-CFAR radar detection using information theoretic criteria,” Prog. Electromag. Res. B, vol. 30, pp.157-175, May 2011.
  7. J. Kefeng, X. Xiangwei, Z. Huanxin, et al., “A novel variable index and excision CFAR based ship detection method on SAR imagery,” J. Sens., pp. 1-10, 2015.
  8. G. Gao, “Statistical modelling of SAR images: a survey,” Sensors, vol. 10, pp. 775-795, 2010.
  9. G. Gao, S. Gao, J. Ouyang, et al, “Scheme for characterizing clutter statistics in SAR amplitude images by combining two parametric models,” IEEE Trans. Geosci. Rem. Sens., vol. 56, no. 10, pp. 5636-5646, 2018.
  10. G. G. Acosta, S. A. Villar, “Accumulated CA-CFAR process in 2-D for online object detection from sidescan sonar data,” IEEE J. Ocean. Eng., vol. 40, no. 3, pp. 558-569, 2015.
  11. S. Meng, K. Ren, D. Lu, et al., “A novel ship CFAR detection algorithm based on adaptive parameter enhancement and wake-aided detection in SAR images,” Infra. Phys. Tech., vol. 89, pp. 263-270, 2018.
  12. M. A. Richards, Fundamentals of radar processing, 2nd Edition, Mcgraw-Hill, 2014, ch. 7, pp. 364-374
  13. G. Gao, L. Liu, L. Zhao, et al., “An adaptive and fast CFAR algorithm based on automatic censoring for target detection in high-resolution SAR images,” IEEE Trans. Geosci. Rem. Sens., vol. 47, no. 6, pp. 1685-1697, 2009.
  14. G. Gao, Characterizing of SAR clutter and its application to land and ocean observations, Springer-Verlag, 2019, ch. 2, pp 53-70
  15. R. Toreinia, M. Kazerooni, M. Fallah, “A novel method in sea clutter and target simulation of forward-looking synthetic aperture radar,” J. Appl. Electromag. (in Persian), vol. 4, no. 1, pp. 9-20, 2016.
  16. X. Leng, K. Ji, X. Xing, et al., “Area ratio invariant feature group for ship detection in SAR Imagery” IEEE J. Sel. Top. Appl. Earth Observ. Rem. Sens., vol. 11, no. 7, pp. 2376-2388, 2018.