Most CFAR detectors need prior information on interfering targets in a multi-target environment

and hence can not keep stable detection performance when the detection environment changes. A new adaptive constant false alarm rate (CFAR) detector，referred as stepwise cumulation CA (SCCA) CFAR detector

is presented for target detection in a multi-target environment for SAR imagery. By employing a cell-to-cell criterion for accepting or rejecting reference samples according to an adaptive threshold

that is

clutter power estimation and standard variance which are independent of interfering targets

the samples from interfering targets are censored and the clutter samples are accumulated stepwisely. The final estimate of the noise level in the cell under test is formed using cell-averaging method with the accumulated homogeneous clutter samples. The detection performance and runtime of SCCA-statistics-based two-parameter CFAR detector are tested and compared with CA-CFAR and OS-CFAR by simulation

and the result shows the proposed detector increases the detection probability

and its runtime approximates that of OS-CFAR.