Showing papers by "John B. Moore published in 2002"

Direct Kalman filtering approach for GPS/INS integration

Abstract: We present a novel Kalman filtering approach for GPS/INS integration. In the approach, GPS and INS nonlinearities are preprocessed prior to a Kalman filter. The GPS preprocessed data are taken as measurement input, while the INS preprocessed data are taken as additional information for the state prediction of the Kalman filter. The advantage of this approach, over the well-studied (extended) Kalman filtering approaches is that a simple and linear Kalman filter can be implemented to achieve significant computation saving with very competitive performance figures.

Quadratically convergent algorithms for optimal dextrous hand grasping

Abstract: There is a robotic balancing task, namely real-time dextrous-hand grasping, for which linearly constrained, positive definite programming gives a quite satisfactory solution from an engineering point of view. We here propose refinements of this approach to reduce the computational effort. 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.

Gradient Flows Computing the C -numerical Range with Applications in NMR Spectroscopy

Abstract: In this paper gradient flows on unitary matrices are studied that maximize the real part of the C-numerical range of an arbitrary complex n×n-matrix A. The geometry of the C-numerical range can be quite complicated and is only partially understood. A numerical discretization scheme of the gradient flow is presented that converges to the set of critical points of the cost function. Special emphasis is taken on a situation arising in NMR spectroscopy where the matrices C,A are nilpotent and the C-numerical range is a circular disk in the complex plane around the origin.

On‐line almost‐sure parameter estimation for partially observed discrete‐time linear systems with known noise characteristics

Abstract: In this paper we discuss parameter estimators for fully and partially observed discrete-time linear stochastic systems (in state-space form) with known noise characteristics. We propose finite-dimensional parameter estimators that are based on estimates of summed functions of the state, rather than of the states themselves. We limit our investigation to estimation of the state transition matrix and the observation matrix. We establish almost-sure convergence results for our proposed parameter estimators using standard martingale convergence results, the Kronecker lemma and an ordinary differential equation approach. We also provide simulation studies which illustrate the performance of these estimators. Copyright © 2002 John Wiley & Sons, Ltd.

On-line almost-sure parameter estimation for partially observed discrete-time linear systems with known noise characteristics

Abstract: In this paper we discuss parameter estimators for fully and partially observed discrete-time linear stochastic systems (in state-space form) with known noise characteristics. We propose finite-dimensional parameter estimators that are based on estimates of summed functions of the state, rather than of the states themselves. We limit our investigation to estimation of the state transition matrix and the observation matrix. We establish almost-sure convergence results for our proposed parameter estimators using standard martingale convergence results, the Kronecker lemma and an ordinary differential equation approach. We also provide simulation studies which illustrate the performance of these estimators. Copyright © 2002 John Wiley & Sons, Ltd.