Title :
Online identification of non-minimum phase finite impulse response linear systems with discrete inputs via hidden Markov models
Author_Institution :
Defence Sci. & Technol. Organ., Salisbury, SA, Australia
Abstract :
Considers adaptive estimation of noisy finite impulse response linear systems driven by stationary inputs from a discrete set. An algorithm based on an equivalent hidden Markov model representation is presented. The algorithm presented is globally convergent while previous approaches have only been locally convergent
Keywords :
adaptive estimation; identification; linear systems; transient response; discrete inputs; globally convergent algorithms; nonminimum phase finite impulse response linear systems; online identification; stationary inputs; Adaptive estimation; Deconvolution; Ear; Finite impulse response filter; Hidden Markov models; Indium phosphide; Linear systems; Space stations; Stochastic processes; Vectors;
Conference_Titel :
Decision and Control, 2000. Proceedings of the 39th IEEE Conference on
Conference_Location :
Sydney, NSW
Print_ISBN :
0-7803-6638-7
DOI :
10.1109/CDC.2001.914670
