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
Journal ArticleDOI
On hidden fractal model signal processing
TL;DR: This paper reformulates this hidden fractal model (HFM) problem in the scalar case as a higher order scalar or first order 2-vector homogeneous hidden Markov model ( HMM) problem, and can apply HMM signal processing techniques to obtain optimal estimates of the signals and signal model parameters, including transition probabilities and noise statistics.
Journal ArticleDOI
Doubly coprime factorizations, reduced-order observers, and dynamic state estimate feedback
Andrew J. Telford,John B. Moore +1 more
TL;DR: In this paper, a doubly coprime factorization of the transfer function of a lumped linear time-invariant system is developed based on minimal-order observers, and it is proved that the class of proper stabilizing controllers for a given plant can be generated by dynamic feedback of the reduced-order state estimate.
Journal ArticleDOI
On improving control-loop robustness of model-matching controllers
TL;DR: In this article, the class of all stabilizing controllers for a two-degree-of-freedom control system which achieve a prescribed achievable transfer function is characterized, and robust model matching is formulated as a standard Hm-optimization problem.
Journal ArticleDOI
Fixed-Lag Smoothing for Nonlinear Systems with Discrete Measurements*
John B. Moore,Peter K. S. Tam +1 more
TL;DR: Approximate nonlinear filtering formulas are applied to yield fixed-lag smoothing results for nonlinear systems with discrete noisy observations, and assuming that the simplifications are valid, then the fixed- lag smoothed estimate is a better one than simply a filtered estimate.
Pose Estimation via Gauss-Newton-on-manifold
Pei Yean Lee,John B. Moore +1 more
TL;DR: A key feature of the proposed approach, not used in earlier studies, is an analytic geodesic search, alternating between gradient, Gauss-Newton and a random direction, which ensures the escape from local minima and convergence to a global minimum without the need to reinitialize the algorithm.