Title :
Convergence analysis of the sign algorithm for adaptive filtering
Author :
Masry, Elias ; Bullo, Francesco
Author_Institution :
Dept. of Electr. & Comput. Eng., California Univ., San Diego, La Jolla, CA, USA
fDate :
3/1/1995 12:00:00 AM
Abstract :
We consider the convergence analysis of the sign algorithm for adaptive filtering when the input processes are uncorrelated and Gaussian and a fixed step size μ>0 is used. Exact recursive equations for the covariance matrix of the deviation error are established for any step size μ>0. Asymptotic time-averaged convergence for the mean-absolute deviation error, mean-square deviation error, and for the signal mean-square estimation error are established. These results are shown to hold for arbitrary step size μ>0
Keywords :
Gaussian processes; adaptive estimation; adaptive filters; convergence of numerical methods; covariance matrices; error analysis; filtering theory; adaptive filtering; adaptive linear estimation; asymptotic time-averaged convergence; convergence analysis; covariance matrix; exact recursive equations; input processes; mean-absolute deviation error; mean-square deviation error; sign algorithm; signal mean-square estimation error; step size; uncorrelated Gaussian input process; Adaptive filters; Algorithm design and analysis; Convergence; Covariance matrix; Equations; Estimation error; Filtering algorithms; Least squares approximation; Noise cancellation; Nonlinear filters;
Journal_Title :
Information Theory, IEEE Transactions on
