Abstract
The minimum average correlation energy (MACE) filter, which is linear and shift invariant, has been used extensively in the area of automatic target detection and recognition (ATD/R). We present a nonlinear extension of the MACE filter, based on a statistical formulation of the optimization criterion, of which the linear MACE filter is a special case. A method by which nonlinear topologies can be incorporated into the filter design is presented and adaptation issues are discussed. In particular, we outline a method by which training exhaustively over the image plane is avoided which leads to much shorter adaptation. Experimental results, using target chips from 35 GHz TABILS 24 inverse synthetic aperture (ISAR) data are presented and performance comparisons are made between the MACE filter and this nonlinear extension.
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