In SAR images

the goodness-of-fit of distribution model to SAR clutter data has great effect on the performance of statistical based CFAR (constant false alarm rate) target detection algorithms. In extreme nonhomogeneous region

the common distribution models can not describe the clutter data accurately due to strong impulsive interference. Whereas

α stable distribution which is based on Generalized Central Limit theorem can model the strong impulsive interference accurately and is fit for both homogeneous and nonhomogeneous clutter regions. So a CFAR detection algorithm using α stable distribution is studied in this paper and some key steps

such as parameter estimating

model standardization and threshold calculating is also included. The results on real data show that the SαS CFAR has good performance and can achieve high detection rate and low false alarm rate.