Obviously navigation is one of the most complicated issues in mobile robots. Intelligent algorithms are often used for error handling in robot navigation. This Paper deals with the problem of Inertial Measurement Unit (IMU) error handling by using Extended Kalman Filter (EKF) as an Expert Algorithms. Our focus is put on the field of mobile robot navigation in the 2D environments. The main chall...

In high dynamic conditions, using vector tracking loops instead of scalar tracking loops in GPS receivers is proved as an efficient method to compensate the performance. The Minimum Mean Squared Error detector as a multiuser detector is applied in the vector tracking loop for more reliability and efficiency. The Kalman filter does the two tasks of tracking and extracting the navigation data aft...

Control of a class of uncertain nonlinear systems, which estimates unavailable state variables, is considered. A new approach for robust tracking control problem of satellite for large rotational maneuvers is presented in this paper. The features of this approach include a strong algorithm to estimate attitude, based on discrete extended Kalman filter combined with a continuous extended Kalman ...

in this study, the problem of navigation in dynamic and unknown environment is investigated and a navigation method based on force field approach is suggested. it is assumed that the robot performs navigation in unknown environment and builds the map through slam procedure. since the moving objects' location and properties are unknown, they are identified and tracked by kalman filter. kalm...

We tackle the problem of real-time statistical analysis of functional magnetic resonance imaging (fMRI) data. In a recent paper, we proposed an incremental algorithm based on the extended Kalman filter (EKF) to fit fMRI time series in terms of a general linear model with autoregressive errors (GLM-AR model). We here improve the technique using a new Kalman filter variant specifically tailored t...

SLAM (Simultaneous Localization and Mapping) is a fundamental problem when an autonomous mobile robot explores an unknown environment by constructing/updating the environment map and localizing itself in this built map. The all-important problem of SLAM is revisited in this paper and a solution based on Adaptive Unscented Kalman Filter (AUKF) is presented. We will explain the detailed algorithm...