Trainable FIR-order statistic hybrid filters

Author

Inkinen, Sami J. ; Niittylahti, Jarkko

Author_Institution

Eur. Lab. for Particle Phys., CERN, Geneva, Switzerland

Volume

42

Issue

10

fYear

1995

fDate

10/1/1995 12:00:00 AM

Firstpage

663

Lastpage

666

Abstract

In this paper, an optimization algorithm for FIR-order statistic hybrid (FIR-OS) filters is introduced. The algorithm minimizes the total cost function of the filter output by dividing the training set into subsets using soft order statistics criteria and then applying conjugate gradient search for the subfilters. The amplitude extraction of pulses acquired from high energy physics detectors is presented as an application example. The trained FIR-OS filter is shown to give a precise amplitude estimate in the presence of sample timing jitter

Keywords

FIR filters; adaptive filters; circuit optimisation; conjugate gradient methods; jitter; FIR-order statistic hybrid filters; amplitude extraction; conjugate gradient search; filter output; optimization algorithm; precise amplitude estimate; soft order statistics criteria; subfilters; timing jitter; total cost function; training set; Adaptive filters; Adaptive signal detection; Amplitude estimation; Cost function; Detectors; Finite impulse response filter; Least squares approximation; Statistics; Timing jitter; Working environment noise;

fLanguage

English

Journal_Title

Circuits and Systems II: Analog and Digital Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1057-7130

Type

jour

DOI

10.1109/82.471394

Filename

471394