TY - JOUR
T1 - OPTIMAL INTERPOLATION FOR LINEAR STOCHASTIC SYSTEMS
T2 - THE DISCRETE TIME CASE.
AU - Kohlmann, Michael
AU - Pavon, Michele
PY - 1984
Y1 - 1984
N2 - The least-squares estimation of the state of a linear discrete-time stochastic system when there is a data gap is considered. The approach, hinging on some basic concepts from stochastic realization theory, makes it possible to derive compact expressions for the optimal estimate in a more direct and illuminating way than would be possible via the present smoothing formulas. As a by-product, the estimation problem for the missing observations is solved.
AB - The least-squares estimation of the state of a linear discrete-time stochastic system when there is a data gap is considered. The approach, hinging on some basic concepts from stochastic realization theory, makes it possible to derive compact expressions for the optimal estimate in a more direct and illuminating way than would be possible via the present smoothing formulas. As a by-product, the estimation problem for the missing observations is solved.
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U2 - 10.1109/cdc.1984.272304
DO - 10.1109/cdc.1984.272304
M3 - Conference article
AN - SCOPUS:0021642116
SN - 0191-2216
SP - 1479
EP - 1483
JO - Proceedings of the IEEE Conference on Decision and Control
JF - Proceedings of the IEEE Conference on Decision and Control
ER -