Complex systems are often described with competing models. Such divergence of interpretation on the system may stem from model fidelity, mathematical simplicity, and more generally, our limited knowledge of the underlying processes. Meanwhile, available but limited observations of system state could further complicates one’s prediction choices. Over the years, data assimilation techniques, such...

A necessary ingredient of an ensemble Kalman filter is covariance inflation [1], used to control filter divergence and compensate for model error. There is an ongoing search for inflation tunings that can be learned adaptively. Early in the development of Kalman filtering, Mehra [2] enabled adaptivity in the context of linear dynamics with white noise model errors by showing how to estimate the...

Using direction vectors of unit length as measurements for attitude estimation in an extended Kalman filter inevitably results in a singular measurement covariance matrix. Singularity of the measurement covariance means no noise is present in one component of the measurement. Unit vector measurements have no noise in the radial component. Singular measurement covariances can be dealt with by th...

The Kalman filter is an established tool for the analysis of dynamic systems with normally distributed noise, and it has been successfully applied in numerous application areas. It provides sequentially calculated estimates of the system states along with a corresponding covariance matrix. For nonlinear systems, the extended Kalman filter is often used which is derived from the Kalman filter by...

This paper presents an experimental study on impedance control in both Cartesian and object level with adaptive friction compensation for dexterous robot hand based on joint torque feedback. To adaptively decrease the effects of high friction caused by complex transmission systems and joint coupling, a friction observer is proposed based on the extended Kalman filter (EKF) in this paper. A Cart...

Our primal goal in this project is to keep track of an object of interest in a sequence of images. We assume that a camera is fixed, the target is a single solid object, and (partial) image of the target is provided. We applied two different approaches: parametric and non-parametric estimation techniques. The comparison of these approaches shows interesting results (e.g., how well they perform ...

Performance of any tracking algorithm depends upon the model selected to capture the target dynamics. In real world applications, no apriori knowledge about the target motion is available. Moreover, it could be a maneuvering target. The proposed method is able to track maneuvering or nonmaneuvering multiple point targets with large motion ( pixels) using multiple filter bank in an IR image sequ...

In this paper, we propose a general model for representing sensorial information and its uncertainty, the Symmetries and Perturbation model (SPmodel). In it, the intrinsic partiality of geometric information is represented in terms of the symmetries of the involved geometric elements. Location uncertainty due to sensor imprecision is represented by means of a local perturbation, expressed in a ...

Indoor localization of mobile agents using wireless technologies is becoming very important in military and civil applications. This paper introduces an approach for the indoor localization of a mobile agent based on Ultra-WideBand technology using a Biased Extended Kalman Filter (EKF) as a possible technique to improve the localization. The proposed approach allows to use a low-cost IMU (inert...