Recursive local orthogonality filtering

Author

Bodenschatz, John S. ; Nikias, Chrysostomos L.

Author_Institution

Dept. of Electr. Eng., Univ. of Southern California, Los Angeles, CA, USA

Volume

45

Issue

9

fYear

1997

fDate

9/1/1997 12:00:00 AM

Firstpage

2293

Lastpage

2300

Abstract

Recursive local orthogonality (RLO) is a stochastic Newton algorithm to achieve a condition we term local orthogonality, which only requires unimodal symmetric densities with continuous nonzero second derivatives near the origin. For Gaussian systems, RLO reduces to recursive least squares. Local orthogonality is both a subset of median orthogonality and a form of constrained maximum-likelihood optimization. Fast multichannel time and multichannel frequency domain implementations are given. Simulations show the utility for system identification and inverse modeling

Keywords

Gaussian processes; Newton method; frequency-domain analysis; identification; inverse problems; least squares approximations; maximum likelihood estimation; optimisation; recursive filters; time-domain analysis; Gaussian systems; RLO; constrained maximum-likelihood optimization; continuous nonzero second derivatives; inverse modeling; median orthogonality; multichannel frequency domain implementation; recursive least squares; recursive local orthogonality filtering; stochastic Newton algorithm; system identification; time domain implementation; unimodal symmetric densities; Equations; Filtering; Finite impulse response filter; Frequency domain analysis; Inverse problems; Least squares methods; Stochastic processes; System identification; Transversal filters; Working environment noise;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.622951

Filename

622951