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.
© 2005 Optical Society of America
(070.2580) Fourier optics and signal processing : Paraxial wave optics
(070.2590) Fourier optics and signal processing : ABCD transforms
(070.4340) Fourier optics and signal processing : Nonlinear optical signal processing
(100.5010) Image processing : Pattern recognition
(150.0150) Machine vision : Machine vision
Kaveh Heidary and H. John Caulfield, "Application of supergeneralized matched filters to target classification," Appl. Opt. 44, 47-54 (2005)