in the several past years, extended kalman filter (ekf) and unscented kalman filter (ukf) havebecame basic algorithm for state-variables and parameters estimation of discrete nonlinear systems.the ukf has consistently outperformed for estimation. sometimes least estimation error doesn't yieldwith ukf for the most nonlinear systems. in this paper, we use a new approach for a two variablesta...

We present a coupled finite element, Kalman filter approach to foresee impactinduced delamination of layered composites when mechanical properties are partially unknown. Since direct numerical simulations, which require all the constitutive parameters to be assigned, cannot be run in such cases, an inverse problem is formulated to allow for modeling as well as constitutive uncertainties. Upon s...

The Sigma-Point Kalman Filters (SPKF) is a family of filters that achieve very good performance when applied to time series. Currently most researches involving time series forecasting use the Sigma-Point Kalman Filters, however they do not use an ensemble of them, which could achieve a better performance. The REC analysis is a powerful technique for visualization and comparison of regression m...

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...

Autonomous soaring is a promising approach to augment the endurance of small UAVs. Most of the existing work on this field relies on accelerometers and/or GPS receivers to sense thermals in the proximity of the vehicle. However, thermal updrafts are often visually indicated by cumulus clouds that are well characterized by their sharp baselines. This paper focuses on a cloud mapping algorithm wh...

The interval Kalman filter is a variant of the traditional Kalman filter for systems with bounded parametric uncertainty. For such systems, modelled in terms of intervals, the interval Kalman filter provides estimates of the system state also in the form of intervals, guaranteed to contain the Kalman filter estimates of all point-valued systems contained in the interval model. However, for prac...

In order to address the highly nonlinear dynamics in estuary flow, we propose a novel data assimilation system based on components designed to accurately capture nonlinear dynamics. The core of the system is a sigma-point Kalman filter coupled to a fast, neural network emulator of the flow dynamics. In order to be computationally feasible, the entire system operates on a low-dimensional subspac...

This paper describes the design of a tightly coupled GPS/INS integration system based on nonlinear Kalman filtering methods. The traditional methods include linearization of the system around a nominal trajectory, and the extended Kalman filtering (EKF) method which linearizes the system around the previous estimate, or the predication, whichever is available. The recently proposed sigma-point ...