The cubature Kalman filter (CKF) is a relatively new addition to derivative-free approximate Bayesian filters built under the Gaussian assumption. This paper extends the CKF theory to address nonlinear smoothing problems; the resulting state estimator is named the fixed-interval cubature Kalman smoother (FI-CKS). Moreover, the FI-CKS is reformulated to propagate the square-root error covariance...

In addition to its technical merits as a challenging non-rigid motion and structural integrity analysis problem, quantitative estimation of cardiac regional functions and material characteristics has significant physiological and clinical values. We have earlier developed a stochastic finite element framework for the simultaneous estimation of myocardial motion and material parameters from medi...

Optimal control theory and machine learning techniques are combined to formulate and solve in closed form an optimal control formulation of online learning from supervised examples with regularization of the updates. The connections with the classical linear quadratic gaussian (LQG) optimal control problem, of which the proposed learning paradigm is a nontrivial variation as it involves random ...

This paper describes the development of a modified Kalman filter to integrate a multi-camera vision system and strapdown inertial navigation system (SDINS) for tracking a hand-held moving device for slow or nearly static applications over extended periods of time. In this algorithm, the magnitude of the changes in position and velocity are estimated and then added to the previous estimation of ...

Abstract Air-pollution modelling at the local scale requires accurate meteorological inputs such as from velocity field. These fields are generally simulated with microscale models (here Code_Saturne ), which forced boundary conditions provided by larger or observations. Local atmospheric simulations very sensitive to conditions, whose estimation is difficult but crucial. When observations of w...

We congratulate the authors with this important work on capturing latent structures in time series. The paper and it's predecessors are prominent contributions in the exploration of the applicability of dynamic models. The dynamic approach to time series, longitudinal and spatio-temporal problems is in our opinion the natural one. It provides an appealing conceptualisation of the application in...

Mobile mapping systems have been widely applied for acquiring spatial information in applications such as spatial information systems and 3D city models. Nowadays the most common technologies used for positioning and orientation of a mobile mapping system include a Global Positioning System (GPS) as the major positioning sensor and an Inertial Navigation System (INS) as the major orientation se...