Title :
Minimum variance signal estimation with adaptive order statistic filters
Author :
Clarkson, Peter M. ; Williamson, Geoffrey A.
Author_Institution :
Dept. of Electr. & Comput. Eng., Illinois Inst. of Technol., Chicago, IL, USA
Abstract :
The authors consider the estimation of a locally constant signal embedded in stationary noise with unknown statistics. They develop iterative algorithms, dubbed adaptive order statistic filters, designed to approximate the minimum variance unbiased order statistic estimator for the signal. The authors give conditions for convergence in the mean to the optimal estimator, discuss convergence rates, and present supporting simulations
Keywords :
adaptive filters; convergence of numerical methods; filtering and prediction theory; iterative methods; noise; statistical analysis; adaptive order statistic filters; convergence rates; iterative algorithms; locally constant signal; minimum variance unbiased order statistic estimator; signal estimation; simulations; stationary noise; Adaptive filters; Convergence; Estimation; Iterative algorithms; Lagrangian functions; Noise measurement; Nonlinear filters; Q measurement; Statistics; Vectors;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1992. ICASSP-92., 1992 IEEE International Conference on
Conference_Location :
San Francisco, CA
Print_ISBN :
0-7803-0532-9
DOI :
10.1109/ICASSP.1992.226438
