TY - JOUR
T1 - Game theory approach to optimal linear estimation in the minimum H∞-norm sense
AU - Yaesh, I.
AU - Shaked, U.
PY - 1989
Y1 - 1989
N2 - A game theory approach to optimal state estimation is presented. It is found that under certain conditions a min-max estimation is identical to the optimal estimation in the minimum H∞-norm sense. These conditions are similar to those obtained by M. Mintz (J. Optim. Theory Appl., vol. 9, pp. 99-111, 1972), where the relationship between Kalman filtering and the min-max terminal state estimation has been explored. This new interpretation of H∞-optimal state estimation provides insight into the mechanism of H∞-optimal filtering.
AB - A game theory approach to optimal state estimation is presented. It is found that under certain conditions a min-max estimation is identical to the optimal estimation in the minimum H∞-norm sense. These conditions are similar to those obtained by M. Mintz (J. Optim. Theory Appl., vol. 9, pp. 99-111, 1972), where the relationship between Kalman filtering and the min-max terminal state estimation has been explored. This new interpretation of H∞-optimal state estimation provides insight into the mechanism of H∞-optimal filtering.
UR - http://www.scopus.com/inward/record.url?scp=0024924623&partnerID=8YFLogxK
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AN - SCOPUS:0024924623
SN - 0191-2216
VL - 1
SP - 421
EP - 425
JO - Proceedings of the IEEE Conference on Decision and Control
JF - Proceedings of the IEEE Conference on Decision and Control
T2 - Proceedings of the 28th IEEE Conference on Decision and Control. Part 1 (of 3)
Y2 - 13 December 1989 through 15 December 1989
ER -