An off-line smoother algorithm is proposed to estimate foot motion using an inertial sensor unit (three-axis gyroscopes and accelerometers) attached to a shoe. The smoother gives more accurate foot motion estimation than filter-based algorithms by using all of the sensor data instead of using the current sensor data. The algorithm consists of two parts. In the first part, a Kalman filter is use...

In this paper, we propose a robust Kalman filter and smoother for the errors-invariables (EIV) state space model subject to observation noise with outliers. We introduce the EIV problem with outliers and then we present the minimum covariance determinant (MCD) estimator which is highly robust estimator to detect outliers. As a result, a new statistical test to check the existence of outliers wh...

An approach to enhancing a model-based predictive controller by Kalman filter is proposed. The controller uses an ARX process model and the structure of the controller is assumed fixed; some of its internal variables – past values of controlled variables (output history) are accessible and can be modified to achieve better performance in disturbance attenuation and noise rejection. We present a...

A novel algorithm is proposed in this paper to solve blind source separation of post-nonlinear convolutive mixtures of non-stationary sources. Both convolutive mixing and post-nonlinear distortion are included in the proposed model. Based on the generalized Expectation-Maximization (EM) algorithm, the Maximum Likelihood (ML) approach is developed to estimate the parameters in the model. A set o...

Introduction: In the case that the state-space model class is nonlinear, Kalman filter and RTS smoother breaks down. Although it is always possible to linearize the nonlinear model so that Kalman filter and RTS smoother can still apply approximately, they can be not reliable. On the other hand, stochastic simulation approaches are not limited to linear model classes and can be adopted to draw s...

A basic sh-stock assessment system requires an integrated use of a model for the time evolution of a sh stock and information about the sh stock from catch data. Typical for common sh stock assessment systems have been the use of fairly sim-plistic data assimilation methodologies for the integration of observations with the dynamical models. On the other hand, there has been a fast development ...

A prevalent problem in statistical signal processing, applied statistics, and time series analysis is the calculation of the smoothed posterior distribution, which describes the uncertainty associated with a state, or a sequence of states, conditional on data from the past, the present, and the future. The aim of this paper is to provide a rigorous foundation for the calculation, or approximati...

This paper constructs an ensemble-based sampling smoother for fourdimensional data assimilation using a Hybrid/Hamiltonian Monte-Carlo approach. The smoother samples efficiently from the posterior probability density of the solution at the initial time. Unlike the well-known ensemble Kalman smoother, which is optimal only in the linear Gaussian case, the proposed methodology naturally accommoda...