RECENT ADVANCES TO NONLINEAR MINIMUM AVERAGE CORRELATION-ENERGY FILTERS

We present recent advances in the development of nonlinear extensions to the minimum average correlation energy (MACE) filter. The MACE filt er and its variations have been applied to the area of automatic targe t 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 s pecial case. The method by which nonlinear topologies can be incorpora ted 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 method s described result in faster convergence times and significantly bette r classification performance. (C) 1997 Society of Photo-Optical Instru mentation Engineers.