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
More filters
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Persistence of excitation in extended least squares
TL;DR: In this paper, the authors translate these "persistency of excitation" conditions into "sufficiently rich" conditions on the plant noise and inputs, and show that with sufficiently rich inputs, guaranteed convergence rates of prediction errors improve.
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Quadratically convergent algorithms for optimal dextrous hand grasping
TL;DR: The refinements include elimination of structural constraints in the positive definite matrices, orthogonalization of the grasp maps, and giving a precise Newton step size selection rule.
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Improved extended Kalman filter design for passive tracking
Haim Weiss,John B. Moore +1 more
TL;DR: In this paper, an extended Kalman filter is used for coordinate estimation of a stationary object using bearing measurements taken from a moving platform, and a bound on the Lyapunov function decay rate is given to assist in the design of the modified nonlinearities and in the selection of an appropriate coordinate basis.
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Brief paper: Model approximations via prediction error identification
TL;DR: The communication theoretic issue of convergence of a posteriori densities when Bayesian estimation is being undertaken with a finite model set is examined.
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On-line identification of hidden Markov models via recursive prediction error techniques
TL;DR: An on-line state and parameter identification scheme for hidden Markov models (HMMs) with states in a finite-discrete set is developed using recursive prediction error (RPE) techniques and an improved version of an earlier proposed scheme is presented with a parameterization that ensures positivity of transition probability estimates.