Automatic Censoring Detection Using Binary Clutter-Map Estimation for NonGaussian Environments

In this paper, we propose an automatic censoring detector using binary clutter map estimation, for multiple target situations and compound dominated-clutters. The proposed censoring procedure dynamically discards the unwanted samples by implementing an adaptive pre-thresholding, for the incoming clutter echoes. Thus, the obtained decisions are sent serially into a shift register to form the binary clutter map (BCM) estimation. Finally, the logical states of the clutter map register are used to yield the background level estimation. To show the robustness of the clutter map-based detection scheme, the effects of speckle correlation and interfering targets on the censoring efficiency are analyzed.