A new FSII-CFAR detector based on fuzzy membership degree

Yingying Kong; Shu Zhang; Leung Henry

doi:10.1117/12.2300516

27 April 2018 A new FSII-CFAR detector based on fuzzy membership degree

Yingying Kong, Shu Zhang, Leung Henry

Proceedings Volume 10646, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII; 106460W (2018) https://doi.org/10.1117/12.2300516
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

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.

Citation Download Citation

Yingying Kong, Shu Zhang, and Leung Henry "A new FSII-CFAR detector based on fuzzy membership degree", Proc. SPIE 10646, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII, 106460W (27 April 2018); https://doi.org/10.1117/12.2300516

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