title: Application of supergeneralized matched filters to target classification.
abstract: The matched filter (MF) is the optimum linear operator for distinguishing between a fixed signal and noise, given the noise statistics. A generalized matched filter (GMF) is a linear filter that can handle the more difficult problem of a multiple-example signal set, and it reduces to a MF when the signal set has only one member. A supergeneralized matched filter (SGMF) is a set of GMFs and a procedure to combine their results nonlinearly to handle the multisignal problem even better. Obviously the SGMF contains the GMF as a special case. An algorithm for training SGMFs is presented, and it is shown that the algorithm performs quite well even for extremely difficult classification problems.
authors.affiliation: Department of Electrical Engineering, Alabama A&M University, Normal, Alabama 35762, USA. Kheidary@aamu.edu
authors.forename: Kaveh
authors.initials: K
authors.lastname: Heidary
authors.forename: H John
authors.initials: HJ
authors.lastname: Caulfield