The scheme to propagate correlations between online and off-line state variables in atmospheric inversions using the fixed-lag Kalman smoother proposed in Bruhwiler et al. (2005) is explained as a process to impose a balanced constraint on the on-line state variables. It is then extended to the fixed-lag ensemble square root Kalman smoother and fixed-lag square root sigma-point Kalman smoother,...

in many autonomous mobile applications, robots must be capable of analyzing motion of moving objects in their environment. duringmovement of robot the quality of images is affected by quakes of camera which cause high errors in image processing outputs. in thispaper, we propose a novel method to effectively overcome this problem using neural networks and kalman filtering theory. thistechnique u...

obviously navigation is one of the most complicated issues in mobile robots.intelligent algorithms are often used for error handling in robot navigation. thispaper deals with the problem of inertial measurement unit (imu) error handling byusing extended kalman filter (ekf) as an expert algorithms. our focus is put onthe field of mobile robot navigation in the 2d environments. the main challenge...

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...

to perform any economic management of a petroleum reservoir in real time, a predictable and/or updateable model of reservoir along with uncertainty estimation ability is required. one relatively recent method is a sequential monte carlo implementation of the kalman filter: the ensemble kalman filter (enkf). the enkf not only estimate uncertain parameters but also provide a recursive estimate of...

The problem of estimating the number and state of multiple targets using a sensor with limited sensing ability is raised in a variety of applications, including monitoring of endangered species, civilian security, and military surveillance. The particle filter is widely used to solve this problem since Kalman filter’s disadvantage on estimating non-Gaussian distribution. However, the problem be...

in this paper, we deal with the problem of adaptive coherent signal detection in gaussian interference (clutter plus noise) for surveillance pulse radars. some of the adaptive radar detectors exploit the ar model for clutter. most of these detectors have been obtained using the glr test. this test relies on the maximum likelihood estimation whose accuracy depends on the number of data. whereas,...

The paper deals with a state estimation of nonlinear stochastic dynamic systems subject to a nonlinear inequality constraint. A special focus is paid to particle filters, which provide an estimate of the whole probability density as opposed to the local filters, such as the extended Kalman filter or the unscented Kalman filter, which provide a point estimate only. Within the particle filtering ...

Minimizing forecast error requires accurately specifying the initial state from which the forecast is made by optimally using available observing resources to obtain the most accurate possible analysis. The Kalman filter accomplishes this for linear systems and experience shows that the extended Kalman filter also performs well in nonlinear systems. Unfortunately, the Kalman filter and the exte...