INSTITUTO ARGENTINO DE MATEMATICA ALBERTO CALDERON
Unidad Ejecutora - UE
congresos y reuniones científicas
Adaptive MCA-Matched Filter Algorithms for Binary Detection
FRANCISCO MESSINA; BRUNO CERNUSCHI FRÍAS
Congreso; Simposio Argentino de Tecnología (AST 2013); 2013
In this work, we present a method for signal-to-noise ratiomaximization using a linear filter based on minor component analysis ofthe noise covariance matrix. As we will see, the greatest benefits are obtainedwhen both filter and signal design are treated as a single problem.This general problem is then related to the minimization of the probabilityof error of a digital communication. In particular, the classical binarydetection problem is considered when nonstationary and (possibly) nonwhiteadditive Gaussian noise is present. Two algorithms are given tosolve the problem at hand with cuadratic and linear computational complexitywith respect to the dimension of the problem.