Insulin sensitivity is an important physiological parameter for determining insulin requirements patients with type 1 diabetes. In addition to being highly variable between patients, increases substantially during exercise and stays elevated several hours subsequent recovery. We propose unscented Kalman filter estimating from continuous glucose monitoring data that does not require the underlyi...

This paper presents sensor data fusion using Unscented Kalman Filter (UKF) to implement high performance vestibulo-ocular reflex (VOR) based vision tracking system for mobile robots. Information from various sensors is required to be integrated using an efficient sensor fusion algorithm to achieve a continuous and robust vision tracking system. We use data from low cost accelerometer, gyroscope...

In this paper, the application of the fuzzy interacting multiple model unscented Kalman filter (FUZZY-IMMUKF) approach to integrated navigation processing for the maneuvering vehicle is presented. The unscented Kalman filter (UKF) employs a set of sigma points through deterministic sampling, such that a linearization process is not necessary, and therefore the errors caused by linearization as ...

We describe a multi-modal unscented Kalman filter developed for estimation of aircraft position, velocity and heading from noisy surface surveillance data. The raw data is composed of tracks generated by the Airport Surface Detection Equipment, Model-X at Boston Logan International Airport, and is obtained from the Runway Status Lights system. The multi-modal filter formulation facilitates esti...

Accurate and efficient orbital propagators are critical for space situational awareness because they drive uncertainty propagation which is necessary for tracking, conjunction analysis, and maneuver detection. Existing sigma pointor particle-based methods for uncertainty propagation use explicit numerical integrators for propagating the closely spaced orbital states as part of the prediction st...

The Kalman filter and its nonlinear variants have been widely used for filtering and state estimation. However, models with severe nonlinearities are not handled well by Kalman filters. Such an application is presented in this paper: the estimation of the overflow losses in a hopper-dredger. The overflow mixture density and flow-rate have to be estimated based on noisy measurements of the total...

The Czochralski process is the only method used commercially for production of monocrystalline silicon for semiconductor and solar cell applications. This paper explores the use of mathematical modeling as an aid in estimation of system state variables in the standard Czochralski process. A state-space model of the process is presented, describing the dynamics of the crystal radius and meniscus...

This paper presents sensor data fusion using Unscented Kalman Filter (UKF) to implement high performance vestibulo-ocular reflex (VOR) based vision tracking system for mobile robots. Information from various sensors is required to be integrated using an efficient sensor fusion algorithm to achieve a continuous and robust vision tracking system. We use data from low cost accelerometer, gyroscope...

An adaptive model-based predictive control scheme is proposed for non-linear systems. This methodology exploits the non-linear modelling capabilities of nonlinear state-space neural networks and the online weights adjustment by means of an unscented Kalman filter. Results from experiments show evidences on its good tracking performance even when the system’s dynamics change.