000211499 001__ 211499
000211499 005__ 20190407160257.0
000211499 037__ $$aCONF
000211499 245__ $$aIdentification of Rational Transfer Functions from Sampled Data
000211499 269__ $$a2013
000211499 260__ $$bSampTA$$c2013
000211499 336__ $$aConference Papers
000211499 520__ $$9eng$$a We consider the task of estimating an operator from sampled data. The operator, which is described by a rational transfer function, is applied to continuous-time white noise and the resulting continuous-time process is sampled uniformly. The main question we are addressing is whether the stochastic properties of the time series that originates from the sample values of the process allows one to determine the operator. We focus on the autocorrelation property of the process and identify cases for which the sampling operator is injective. Our approach relies on sampling properties of almost periodic functions, which together with exponentially decaying functions, provide the building blocks of the autocorrelation measure. Our results indicate that it is possible, in principle, to estimate the parameters of the rational transfer function from sampled data, even in the presence of prominent aliasing.
000211499 700__ $$0242489$$aKirshner, H.$$g194014
000211499 700__ $$aWard, J.P.
000211499 700__ $$0240182$$aUnser, M.$$g115227
000211499 773__ $$kBremen, Federal Republic of Germany$$q341–343$$tProceedings of the Tenth International Workshop on Sampling Theory and Applications (SampTA'13)
000211499 8564_ $$uhttp://bigwww.epfl.ch/publications/kirshner1304.html$$zURL
000211499 8564_ $$uhttp://bigwww.epfl.ch/publications/kirshner1304.pdf$$zURL
000211499 8564_ $$uhttp://bigwww.epfl.ch/publications/kirshner1304.ps$$zURL
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