Title :
Trainable FIR-order statistic hybrid filters
Author :
Inkinen, Sami J. ; Niittylahti, Jarkko
Author_Institution :
Eur. Lab. for Particle Phys., CERN, Geneva, Switzerland
fDate :
10/1/1995 12:00:00 AM
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;
Journal_Title :
Circuits and Systems II: Analog and Digital Signal Processing, IEEE Transactions on
