J
John B. Moore
Researcher at Australian National University
Publications - 352
Citations - 19139
John B. Moore is an academic researcher from Australian National University. The author has contributed to research in topics: Adaptive control & Linear-quadratic-Gaussian control. The author has an hindex of 50, co-authored 352 publications receiving 18573 citations. Previous affiliations of John B. Moore include Akita University & University of Hong Kong.
Papers
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Deinterleaving pulse trains using discrete-time stochastic dynamic-linear models
TL;DR: Time-domain techniques for deinterleaving pulse trains from a finite number of periodic sources based on the time of arrival (TOA) and pulse energy, if available, of the pulses received on the one communication channel are proposed.
On L2-Sensitivity Minimization of Linear
Wei-Yong Yan,John B. Moore +1 more
TL;DR: In this paper, a specific form of the solution to the L2-sensitivity mini-mization problem is derived together with a bound, which gives an insight into the difference between a pure L 2 -sensitivity optimal realization and a mixed L 2 / L −sensitivity optimum one.
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Generalizations of singular optimal control theory
P. Moylan,John B. Moore +1 more
TL;DR: In this article, a new approach to the optimization of the linear, possibly time-varying, system [email protected] = Fx + Gu |u"i| @? 1 with respect to the performance index V = @!^t^"^1"t"""0x'Qxdt.
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Risk-sensitive filtering and smoothing for hidden Markov models
Subhrakanti Dey,John B. Moore +1 more
TL;DR: In this paper, the problem of risk-sensitive filtering and smoothing for discrete-time Hidden Markov Models (HMM) with finite-discrete states is addressed, where the objective is to minimize the expectation of the exponential of the squared estimation error weighted by a risk sensitive parameter.
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Enhancement of fixed controllers via Adaptive-Q disturbance estimate feedback
Teng-Tiow Tay,John B. Moore +1 more
TL;DR: The proposed adaptive- Q disturbance estimate feedback (DEF) controllers can be simple to implement even for high order multivariable plants with high order fixed controllers, and have the significance that they seek to enhance performance of standard controller designs in the face of plant perturbations or uncertainties, rather than supplant or compete with them.