Title :
A performance bound for the LMS estimator
Author :
Quirk, Kevin J. ; Milstein, Laurence B. ; Zeidler, James R.
Author_Institution :
Dept. of Electr. & Comput. Eng., California Univ., San Diego, La Jolla, CA, USA
fDate :
5/1/2000 12:00:00 AM
Abstract :
The least-mean-square (LMS) estimator is a nonlinear estimator with information dependencies spanning the entire set of data fed into it. The traditional analysis techniques used to model this estimator obscure these dependencies; to simplify the analysis they restrict the estimator to the finite set of data sufficient to span the length of its filter. Thus the finite Wiener filter is often considered a bound on the performance of the LMS estimator. Several papers have reported the performance of the LMS filter exceeding that of the finite Wiener filter. We derive a bound on the LMS estimator which does not exclude the contributions from data outside its filter length. We give examples of this bound in cases where the LMS estimator outperforms the finite Wiener filter
Keywords :
Wiener filters; adaptive estimation; adaptive filters; adaptive signal processing; filtering theory; least mean squares methods; nonlinear estimation; optimisation; signal representation; spectral analysis; LMS adaptive filter; LMS estimator; filter length; finite Wiener filter; information dependencies; interference suppression; least mean square estimator; noise cancellation; nonlinear estimator; optimal estimator; performance bound; spectral factorization; spectral representations; wide-sense stationary signals; Adaptive filters; Filtering algorithms; Finite impulse response filter; Least squares approximation; Multidimensional systems; Noise cancellation; Nonlinear equations; Performance analysis; Signal processing algorithms; Wiener filter;
Journal_Title :
Information Theory, IEEE Transactions on
