Recurrent neural networks, in contrast to the classical feedforward neural networks, better handle inputs that have spacetime structure, e.g. symbolic time series. Since the classic gradient methods for recurrent neural network training on longer input sequences converge very poorly and slowly, the alternative approaches are needed. We describe the principles of the training method with the Ext...

The Kalman filter has been widely used for estimation and tracking of linear systems since its formulation in 1960 due to its simplicity and robustness. In many chemical engineering applications the extended Kalman filter (EKF) is often used to deal with certain classes of nonlinear systems. In spite of that, designing an EKF for highly nonlinear processes is not a trivial task, particularly th...

The problem of tracking a vessel modelled at a distance by an equivalent magnetic dipole is investigated. Tracking a magnetic dipole from magnetic field measurements is a complex non-linear problem. The determination of target position, velocity and magnetic moment is formulated as an optimal stochastic estimation problem, which could be solved using the non-linear Kalman filtering methods. The...

In the normal operation conditions of a pico satellite, conventional Unscented Kalman Filter (UKF) gives sufficiently good estimation results. However, if the measurements are not reliable because of any kind of malfunction in the estimation system, UKF gives inaccurate results and diverges by time. This study, introduces Robust Unscented Kalman Filter (RUKF) algorithms with the filter gain cor...

Satellite autonomous orbit determination OD is a complex process using filtering method to integrate observation and orbit dynamic equations effectively and estimate the position and velocity of a satellite. Therefore, the filtering method plays an important role in autonomous orbit determination accuracy and time consumption. Extended Kalman filter EKF , unscented Kalman filter UKF , and unsce...