Title :
Dynamic weight leakage for LMS adaptive linear predictors
Author :
Campbell, Duncan A. ; Cahill, Laurence W.
Author_Institution :
Sch. of Electron. Eng., La Trobe Univ., Bundoora, Vic., Australia
Abstract :
Adaptive line enhancers provide a means of adaptively estimating the spectral characteristics of low SNR stationary signals. The LMS algorithm is a computationally efficient method for updating filter weights and is commonly used. Insufficient modal stimulation can lead to false spectral peaks. These peaks may be eliminated by applying fixed weight leakage however this produces a biased spectral estimate. Dynamic weight leakage and hybrid combined weight leakage schemes have been developed and are applied within the LMS structure. They provide mechanisms for suppressing state spectral modes with minimal or no bias in the spectral estimates. These schemes can be implemented with only a minor impact on the computational effort
Keywords :
FIR filters; adaptive estimation; adaptive filters; adaptive signal processing; filtering theory; interference suppression; least mean squares methods; prediction theory; spectral analysis; FIR filter; LMS adaptive linear predictors; LMS algorithm; adaptive estimation; adaptive filter; adaptive line enhancers; biased spectral estimate; computationally efficient method; dynamic weight leakage; false spectral peaks; filter weights updating; fixed weight leakage; hybrid combined weight leakage; low SNR stationary signals; modal stimulation; spectral characteristics; state spectral modes suppression; transversal filter; Adaptive filters; Delay; Equations; Finite impulse response filter; Frequency; Least squares approximation; Line enhancers; Noise cancellation; Nonlinear filters; Transversal filters;
Conference_Titel :
TENCON '96. Proceedings., 1996 IEEE TENCON. Digital Signal Processing Applications
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-3679-8
DOI :
10.1109/TENCON.1996.608405