We demonstrate a three-step method for estimating time-resolved velocity fields using time-resolved point measurements and non-time-resolved particle image velocimetry (PIV) data. First, we use linear stochastic estimation to obtain an initial set of time-resolved estimates of the flow field. These initial estimates are then used to identify a linear model of the flow physics. The model is inco...

In order to optimize surface CO2 fluxes at grid scales, a regional surface CO2 flux inversion system (Carbon Flux Inversion system and Community Multi-scale Air Quality, CFI-CMAQ) has been developed by applying the ensemble Kalman filter (EnKF) to constrain the CO2 concentrations and applying the ensemble Kalman smoother (EnKS) to optimize the surface CO2 fluxes. The smoothing operator is assoc...

This paper provides a gentle introduction to the Kalman filter, a numerical method that can be used for sensor fusion or for calculation of trajectories. First, we consider the Kalman filter for a one-dimensional system. The main idea is that the Kalman filter is simply a linear weighted average of two sensor values. Then, we show that the general case has a similar structure and that the mathe...

An adaptive extended Kalman filter is developed and investigated for a transient heat transfer problem in which a high heat flux spot source is applied on one side of a thin plate and ultrasonic pulse time of flight is measured between spatially separated transducers on the opposite side of the plate. The novel approach is based on the uncertainty in the state model covariance and leverages tre...

Gait phase-based control is a trending research topic for walking-aid robots, especially robotic lower-limb prostheses. phase estimation challenge gait control. Previous researches used the integration or differential of human's thigh angle to estimate phase, but accumulative measurement errors and noises can affect results. In this letter, more robust method proposed using unified form piecewi...

Determining the magnitude and location of neural sources within the brain that are responsible for generating magnetoencephalography (MEG) signals measured on the surface of the head is a challenging problem in functional neuroimaging. The number of potential sources within the brain exceeds by an order of magnitude the number of recording sites. As a consequence, the estimates for the magnitud...

Both ensemble filtering and variational data assimilation methods have proven useful in the joint estimation of state variables and parameters of geophysical models. Yet, their respective benefits and drawbacks in this task are distinct. An ensemble variational method, known as the iterative ensemble Kalman smoother (IEnKS) has recently been introduced. It is based on an adjoint model-free vari...

This article presents a solution to the time series prediction competition of the ESTSP 2007 conference. The solution is based on optimal filtering, which is a methodology for computing recursive solutions to statistical inverse problems, where a time varying stochastic state space model is measured through a sequence of noisy measurements. In the solution, the overall behavior of the time seri...

This paper is devoted to adaptive long autoregressive spectral analysis when i) very few data are available and ii) information does exist beforehand concerning the spectral smoothness and time continuity of the analyzed signals. The contribution is founded on two papers by Kitagawa and Gersch [1], [2]. The first one deals with spectral smoothness in the regularization framework, while the seco...