Abstract
A fuzzy statistical normalization fuzzy constant false alarm rate (FSN-FCFAR) detector in non-Rayleigh background based on fuzzy statistical normalization and fuzzy soft decision is proposed. The performance of the proposed fuzzy soft decision detector is studied both for homogeneous backgrounds and for non-homogeneous environments caused by interfering targets or clutter edges. Performance comparisons with the conventional hard decision CFAR detectors such as CA-CFAR, GO-CFAR and OS-CFAR are carried out. The comparison results show that the proposed FSN-FCFAR detector can not only get a very good detection performance in homogeneous backgrounds, but also can confront interfering targets and clutter edges at the same time in non-homogeneous environments. Moreover, the fuzzy soft decision detector can provide more valuable information than the hard decision detector for data fusion, target tracking or object identification.
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