Least-squares order statistic filters for signal restoration in dependent noise

Author

Naaman, Laith ; Bovik, Alan C.

Author_Institution

Dept. of Electr. & Comput. Eng., Texas Univ., Austin, TX, USA

fYear

1990

fDate

3-6 Apr 1990

Firstpage

1225

Abstract

C.G. Boncelet´s algorithm (SIAM J. Sci. Stat. Comput., vol.8, p.868-76, Sept. 1987) is used to explore the OS filter design/analysis problem. In particular, the optimal filter for restoring nonrandom signals immersed in Markov noise, using the mean square error as an optimality criterion, is studied. The noise processes are modeled either as causal first-order autoregressive Gaussian or as first-order moving-average Gaussian. Various structural signal constraints are improved on the solution by stating them as local unbiasedness constraints

Keywords

Markov processes; digital filters; least squares approximations; random noise; signal processing; time series; Boncelet´s algorithm; Markov noise; first-order autoregressive Gaussian; first-order moving-average Gaussian; least-squares order statistic filters; mean square error; Distributed computing; Estimation theory; Filtering theory; Filters; Gaussian noise; Mean square error methods; Noise robustness; Operating systems; Signal design; Signal restoration; Smoothing methods; Statistics;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1990. ICASSP-90., 1990 International Conference on

Conference_Location

Albuquerque, NM

ISSN

1520-6149

Type

conf

DOI

10.1109/ICASSP.1990.115593

Filename

115593