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...

Localization is an essential element of ensemble-based Kalman filters in largescale systems. Two localization methods are commonly used: Covariance localization and domain localization. The former applies a localizing weight to the forecast covariance matrix while the latter splits the assimilation into local regions in which independent assimilation updates are performed. The domain localizati...

This paper deals with the fusion of random variables when cross covariances are unknown. This is a vital problem in nearly every real world application since cross covariances are often impossible to obtain but also cannot be ignored. We provide a rigorous derivation of the fusion equations which are also known as covariance intersection. This approach allows us to derive an iterative scheme fo...

This paper investigates the effects of spatial filtering on the ensemble based estimate of the background error covariance matrix in an ensemble based Kalman filter (EnKF). In particular, a novel kernel smoothing method with variable bandwidth is introduced and its performance is compared to that of the widely used Gaspari-Cohn filter, which uses a fifth order kernel function with a fixed local...

This paper presents a new filtering algorithm, switching extended Kalman filter bank (SEKFB), for indoor localization using wireless sensor networks. SEKFB overcomes the problem of uncertain process-noise covariance that arises when constant-velocity motion model localization. In several filters (EKFs) run in parallel set hypotheses, and most probable output obtained from EKFs is selected Mahal...

We study the problem of change point localization for covariance matrices in high dimensions. assume that we observe a sequence independent and centered $p$-dimensional sub-Gaussian random vectors whose are piecewise constant, only at unknown times. concerned with task estimating positions points. In our analysis, allow all model parameters to sample size $n$, including dimension $p$, minimal s...

This paper examines the accuracy of audio-video based localization using multiple cameras and multi-microphones. Covariance mapping theory is used to determine the accuracy of audio and video based localization. Both modalities are compared in terms of their ability to provide accurate location estimates of a moving audio-visual source. Relatively, video is found to be significantly more accura...

Improving the performance of ensemble filters applied to models with many state variables requires regularization of the covariance estimates by localizing the impact of observations on state variables. A covariance localization technique based on modeling of the sample covariance with polynomial functions of the diffusion operator (DL method) is presented. Performance of the technique is compa...

Cooperative localization is fundamental to autonomous multirobot systems, but most algorithms couple inter-robot communication with observation, making these susceptible failures in both and observation steps. To enhance the resilience of cooperative a distributed system, we use covariance intersection formalize algorithm an explicit update ensure estimation consistency at same time. We investi...