CFAR Processing with multiple exponential smoothers for nonhomogeneous environments

Gürakan, Berk
Conventional methods of CFAR detection always use windowing, in the sense that some number of cells are investigated and the target present/absent decision is made according to the composition of the cells in that window. The most commonly used versions of CFAR detection algorithms are cell averaging CFAR, smallest of cell averaging CFAR, greatest of cell averaging CFAR and order-statistics CFAR. These methods all use windowing to set the decision threshold. In this thesis, rather than using windowed CFAR algorithms, a new method of estimating the background threshold is presented, analyzed and simulated. This new method is called the Switching IIR CFAR algorithm and uses two IIR filters to accurately estimate the background threshold. Then, using a comparison procedure, one of the filters is selected as the current threshold estimate and used. The results are seen to be satisfactory and comparable to conventional CFAR methods. The basic advantages of using the SIIR CFAR method are computational simplicity, small memory requirement and acceptable performance under clutter edges and multiple targets.


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CFAR processing with switching exponential smoothers for nonhomogeneous environments
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Citation Formats
B. Gürakan, “CFAR Processing with multiple exponential smoothers for nonhomogeneous environments,” M.S. - Master of Science, Middle East Technical University, 2010.