@article{Pin:2016:10.1109/TAC.2015.2434075,
author = {Pin, G and Assalone, A and Lovera, M and Parisini, T},
doi = {10.1109/TAC.2015.2434075},
journal = {IEEE Transactions on Automatic Control},
pages = {360--373},
title = {Non-asymptotic kernel-based parametric estimation of continuous-time linear systems},
url = {http://dx.doi.org/10.1109/TAC.2015.2434075},
volume = {61},
year = {2016}
}

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TY  - JOUR
AB  - In this paper, a novel framework to address the problem of parametric estimation for continuous-time linear time-invariant dynamic systems is dealt with. The proposed methodology entails the design of suitable kernels of non-anticipative linear integral operators thus obtaining estimators showing, in the ideal case, “non-asymptotic” (i.e., “finite-time”) convergence. The analysis of the properties of the kernels guaranteeing such a convergence behaviour is addressed and a novel class of admissible kernel functions is introduced. The operators induced by the proposed kernels admit implementable (i.e., finite-dimensional and internally stable) state-space realizations. Extensive numerical results are reported to show the effectiveness of the proposed methodology. Comparisons with some existing continuous-time estimators are addressed as well and insights on the possible bias affecting the estimates are provided.
AU  - Pin,G
AU  - Assalone,A
AU  - Lovera,M
AU  - Parisini,T
DO  - 10.1109/TAC.2015.2434075
EP  - 373
PY  - 2016///
SN  - 0018-9286
SP  - 360
TI  - Non-asymptotic kernel-based parametric estimation of continuous-time linear systems
T2  - IEEE Transactions on Automatic Control
UR  - http://dx.doi.org/10.1109/TAC.2015.2434075
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000370428800006&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://ieeexplore.ieee.org/document/7109144
UR  - http://hdl.handle.net/10044/1/31301
VL  - 61
ER  - 

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