Share Email Print
cover

Proceedings Paper

A new FSII-CFAR detector based on fuzzy membership degree
Author(s): Yingying Kong; Shu Zhang; Leung Henry
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Since a lot of speckles in SAR images, there are a lot of uncertainty in SAR image. It brings a lot of difficulty to the targets detection. Fuzzy theory is a mathematical method used to reduce this uncertainty. A new FSII-CFAR detector is proposed, which is improved intelligent iterative CFAR detection by searching a better fitting distribution model of SAR image background based on fuzzy logic. The best fitting distribution model of background data is decided by the membership value of fuzzy clustering criterion (FCC). Compared with traditional fitting criterion, the results of the FCC improve the detection rate of CFAR. Because the fitting results are more approximated to SAR image background, the simulation results show that the FSII-CFAR detector can make the detection rate reach more than 80% in complex background.

Paper Details

Date Published: 27 April 2018
PDF: 10 pages
Proc. SPIE 10646, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII, 106460W (27 April 2018); doi: 10.1117/12.2300516
Show Author Affiliations
Yingying Kong, Nanjing Univ. of Aeronautics and Astronautics (China)
Shu Zhang, Nanjing Univ. of Aeronautics and Astronautics (China)
Leung Henry, Univ. of Calgary (Canada)


Published in SPIE Proceedings Vol. 10646:
Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII
Ivan Kadar, Editor(s)

© SPIE. Terms of Use
Back to Top