The ground moving target indicator (GMTI) radar sensor plays an important role in situation awareness of the battlefield, surveillance, and precision tracking of ground targets. The extended Kalman filter (EKF) is usually used either alone or in the Interacting Multiple Model (IMM) framework to solve nonlinear filtering problems. Since the GMTI measurement model is nonlinear, the use of an EKF ...

This paper gives the survey of the existing developments of Visual object target tracking using particle filter from the last decade and discusses the advantage and disadvantages of various particle filters. A variety of different approaches and algorithms have been proposed in literature. At present most of the work in Visual Object Target Tracking is focusing on using particle filter. The par...

The electrical network measurements by measuring device Phasor Measurement Device (PMU) are usually sent to the control centers using data acquisition system and other communication protocols available. However, these measurements contain uncertainties due to the measurements and communication noise (errors), incomplete metering or unavailability of some of measurements. The overall aim of stat...

The Extended Kalman Filter (EKF) has been the de facto approach to the Simultaneous Localization and Mapping (SLAM) problem for nearly fifteen years. However, the EKF has two serious deficiencies that prevent it from being applied to large, realword environments: quadratic complexity and sensitivity to failures in data association. FastSLAM, an alternative approach based on the Rao-Blackwellize...

The standard formulations of the Kalman filter (KF) and extended Kalman filter (EKF) require storing and multiplication of matrices of size n×n, where n is the size of the state space, and the inversion of matrices of size m×m, where m is the size of the observation space. For large dimensions implementation issues arise. In this paper we introduce a Variational Kalman Filter (VKF) method to pr...

This paper evaluates the positioning and tracking performance of Extended Kalman Filter (EKF) in wireless sensors network. The EKF is a linear approximation of statistical Kalman Filter (KF) and has the capability to work efficiently in nonlinear systems. The EKF is based on an iterative process of estimating current state information from the previously estimated state. Its working is based on...

Map-Based Localization approaches use a local map of the sensed environment that is matched against a previously stored map to correct the robot localization in the world. In many cases these methods are based on a probabilistic representation of the spatial uncertainty and use the Kalman Filter (KF) or the Extended Kalman Filter (EKF) to update the robot’s location estimation. On the other han...

Structural system identification using recursive methods has been a research direction of increasing interest in recent decades. The two prominent methods, including the Extended Kalman Filter (EKF) and the Particle Filter (PF), also known as the Sequential Monte Carlo (SMC), are advantageous in this field. In this study, the system identification of a shake table test of a 4-story steel struct...

Parameter errors in orbital models can result in poor orbit determination (OD) using a traditional Kalman filter. One approach to account for these errors is to consider them in the so-called Schmidt-Kalman filter (SKF), by augmenting the state covariance matrix (CM) with additional parameter covariance rather than additively estimating these so-called “consider” parameters. This paper introduc...

Satellites provide various services essential to the modern life of human being. For example, satellite images are used for many applications such as reconnaissance, geographic information system, etc. Therefore, design and operation requirements of the satellite system have become more severe, and also the system reliability during the operation is required. Satellite attitude control systems ...