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
Proceedings ArticleDOI
Recursive algorithms for real-time grasping force optimization
TL;DR: Two versions of strictly convex cost functions including a barrier term, one of them self-concordant, are considered and it is shown that the proposed algorithms guarantee convergence to the unique solution of the underlying semidefinite program.
Journal ArticleDOI
An adaptive hidden Markov model approach to FM and M -ary DPSK demodulation in noisy fading channels
Iain B. Collings,John B. Moore +1 more
TL;DR: Extended Kalman filtering (EKF) and hidden Markov model (HMM) signal processing techniques are coupled in order to demodulate frequency modulated signals in noisy fading channels to help cope with coloured noise.
Journal ArticleDOI
The singular solutions to a singular quadratic minimization problem
TL;DR: In this paper, a number of generalizations of singular quadratic minimization theory for the finite-time case are considered, and new stability results are derived for the infinite-time problem, and novel techniques are introduced to facilitate the derivation of singular solutions.
Journal ArticleDOI
On the estimation of interleaved pulse train phases
T.L. Conroy,John B. Moore +1 more
TL;DR: An on-line method for estimating pulse train phases and fine-tuning pulse repetition frequency (PRF) estimates of a known number of interleaved pulse trains, using an extended Kalman filter.
Proceedings ArticleDOI
Tracking randomly varying parameters-analysis of a standard algorithm
Lei Guo,L. Xia,John B. Moore +2 more
TL;DR: In this article, the tracking error bounds for the unknown parameters were established for the Kalman filter and it was shown that it has quite reasonable tracking properties even in the non-Gaussian case when it is not an optimal filter.