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...

This paper proposes a new covariance matching based technique for blurred image PSF (point spread function) estimation. A patch based image degradation model is proposed for the covariance matching estimation framework. A robust covariance metric which is based on Riemannian manifold is adapted to measure the distance between covariance matrices. The optimal PSF is computed by minimizing the di...

We present a general approach and analytical method for determining a search region for use in guided matching under projective mappings. Our approach is based on the propagation of covariance through a first-order approximation of the error model to define the boundary of the search region for a specified probability and we provide an analytical expression for the Jacobian matrix used in the c...

We consider an important class of dynamic singleinput single-output nonlinear systems where the system model is polynomial in observations but linear in parameters. The investigation is done in the errors-in-variables framework, i.e. both input and output are observed with noise. Assuming white Gaussian measurement noise that is characterized by a magnitude and a covariance structure, we propos...

the error of inertial navigation systems increase versus time, therefore for achieving higher accuracy specially in long time navigations we have to use an aiding system. global positioning system is the best aiding system in this case. in this paper we first simulate a gps and ins; then simulate tightly integration and finally review adaptation method of kalman filtering a fuzzy adaptive kalma...

It is widely believed that learning is due, at least in part, to long-lasting modifications of the strengths of synapses in the brain. Theoretical studies have shown that a family of synaptic plasticity rules, in which synaptic changes are driven by covariance, is particularly useful for many forms of learning, including associative memory, gradient estimation, and operant conditioning. Covaria...