Title :
Micro-statistic LMS filtering
Author :
Chen, Shoupu ; Arce, Gonzalo R.
Author_Institution :
Dept. of Electr. Eng., Delaware Univ., Newark, DE, USA
Abstract :
The theory for the class of microstatistic least-mean-square (LMS) filters which requires the adaptation of the statistical characterization of the set of decomposed signals is developed. The authors develop the theoretical framework for adaptive microstatistic filters for applications where the second-order statistics of the threshold signals are not known, or when they may be nonstationary. A multilevel threshold decomposition is used such that real valued stochastic processes can be filtered and the computation complexity of the algorithm can be arbitrary reduced. The superiority of the new adaptive algorithms is shown analytically as well as by way of simulations
Keywords :
computational complexity; filtering and prediction theory; least squares approximations; stochastic processes; adaptive microstatistic filters; computation complexity; decomposed signals; micro-statistic LMS filtering; multilevel threshold decomposition; real valued stochastic processes; second-order statistics; statistical characterization; Adaptive filters; Equations; Filtering; Least squares approximation; Nonlinear filters; Random processes; Signal design; Signal processing; Signal resolution; Statistics;
Conference_Titel :
Circuits and Systems, 1992. ISCAS '92. Proceedings., 1992 IEEE International Symposium on
Conference_Location :
San Diego, CA
Print_ISBN :
0-7803-0593-0
DOI :
10.1109/ISCAS.1992.230602
