One of the most important problem in target tracking is Line Of Sight (LOS) rate estimation for using from PN (proportional navigation) guidance law. This paper deals on estimation of position and LOS rates of target with respect to the pursuer from available noisy RF seeker and tracker measurements. Due to many important for exact estimation on tracking problems must target position and Line O...

In this paper, we consider the problem of estimating wideband polynomial phase signals in sensor arrays. The estimation is based on the introduction of an exact but nonlinear state space modelization, of the signal, which compels us to use the extended Kalman filter (EKF). Furthermore, a solution to the problem of initialization of the filter is also proposed. Under this solution, the numerical...

In this paper extended Kalman filter (EKF) is reviewed for estimating the rotor position/speed of a vector controlled five-phase induction motor drive. The basic configuration of the Kalman filter is studied and the system vectors and matrices are explained. The EKF equations are made from a d-q-axis model of the five-phase induction motor by considering the rotor speed as a state variable. The...

This article presents a sensorless five level DTC control based on neural networks using Extended Kalman Filter (EKF) applied to Double Star Induction Machine (DSIM). The application of the DTC control brings a very interesting solution to the problems of robustness and dynamics. However, this control has some drawbacks such as the uncontrolled of the switching frequency and the strong ripple t...

This paper presents a new multi-sensor data fusion method based on the combination of wavelet transform (WT) and extended Kalman filter (EKF). Input data are first filtered by a wavelet transform via Daubechies wavelet “db4” functions and the filtered data are then fused based on variance weights in terms of minimum mean square error. The fused data are finally treated by extended Kalman filter...

The development of Kalman filters designed for state estimation of the position and velocity of a spacecraft is attempted and their performance evaluated. Three Kalman Filters are developed, each with its unique characteristics: the Extended Kalman Filter (EKF), the Robust Extended Kalman Filter (REKF) and the Adaptive Robust Extended Kalman Filter (AREKF). The three filters are implemented ass...

Kalman filtering is a method for estimating state variables of a dynamic systems recursively from noise-contaminated measurements. For systems with nonlinear dynamics, a natural extension of the Linear Kalman Filter (LKF), called Extended Kalman filter (EKF) is used. The Kalman filter represents one of the most popular estimation techniques for integrating signals from navigation systems, like ...

A new and fast methodology is discussed as a solution to pinpointing the location of a robot called RobVigil, in a robust way, without environment preparation, even in dynamic scenarios. This solution does not require a high computational power. Firstly, the EKF-SLAM is used to find the location of the vehicle, allowing to map the surrounding area in the 3D space. Afterwards, the constructed ma...

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...

This paper presents a detailed study of the extended Kalman filter (EKF) for estimating the rotor resistance and rotor speed of an induction motor drive. The overall structure of the EKF is reviewed and the various system vectors and matrices are defined. By including the rotor resistance and rotor speed as a state variables, the EKF equations are established from a discrete two-axis model of t...