Grasping force estimation using state-space model and Kalman Filter

Geospatial state space estimation using an Ensemble Kalman Filter

Incorporating temporal (continuous) data into more common discrete data point geospatial models is necessary for dynamic real time model building. The models are otherwise limited in their use for numerical modelling, simulation and the prediction of climatic states over time. By adopting a Bayesian approach it is shown here to be possible to estimate the dynamic behaviour of unobserved climate...

State Space Model Based Channel Estimation using Extended Kalman Filter for Superposition Coded Modulation OFDM System

In this work Extended Kalman Filter(EKF)is implemented for Orthogonal Frequency Division MultiplexingSuperposition Coded Modulation(SCM) scheme. Due to time varying nature of Rayleigh fast fading channel which vitiates the performance of data detection which in turn degrades the performance of OFDM system. An efficient channel estimation technique is necessary. An Extended Kalman filtering algo...

State Space Models and the Kalman Filter

Monitoring Hydraulic Fractures: State Estimation using an Extended Kalman Filter

There is considerable interest in using remote elastostatic deformations to identify the evolving geometry of underground fractures that are forced to propagate by the injection of a high pressure viscous fluid. These so-called hydraulic fractures are used to increase the permeability in oil and gas reservoirs as well as to pre-fracture ore-bodies in preparation for a mining process known as bl...

Approximate conditional least squares estimation of a nonlinear state-space model via an unscented Kalman filter

Nonlinear state-space models driven by differential equations have been widely used in science. Their statistical inference generally requires computing the mean and covariance matrix of some nonlinear function of the state variables, which can be done in several ways. For example, such computations may be approximately done by Monte Carlo, which is rather computationally expensive. Linear appr...