Recent advances to nonlinear minimum average correlation energy filters

Abstract

We present recent advances in the development of nonlinear extensions to the minimum average correlation energy (MACE) filter. The MACE filter and its variations have been applied to the area of automatic target detection and recognition (ATD/R). Nonlinear extensions (Fisher and Principe, 1994) have been presented based on a statistical formulation of the optimization criterion, of which the linear MACE filter is a special case. The method by which nonlinear topologies can be incorporated into the filter design is reviewed. We present recent advances to this nonlinear method as well as new experimental results applying the technique to inverse synthetic aperture radar (ISAR) data. The methods described result in faster convergence times and significantly better classification performance.