4,045 research outputs found
Bibliographic Review on Distributed Kalman Filtering
In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this paper, a bibliographical review on distributed Kalman filtering (DKF) is provided.\ud
The paper contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area
A Survey on Multisensor Fusion and Consensus Filtering for Sensor Networks
Multisensor fusion and consensus filtering are two fascinating subjects in the research of sensor networks. In this survey, we will cover both classic results and recent advances developed in these two topics. First, we recall some important results in the development ofmultisensor fusion technology. Particularly, we pay great attention to the fusion with unknown correlations, which ubiquitously exist in most of distributed filtering problems. Next, we give a systematic review on several widely used consensus filtering approaches. Furthermore, some latest progress on multisensor fusion and consensus filtering is also presented. Finally,
conclusions are drawn and several potential future research directions are outlined.the Royal Society of the UK, the National Natural Science Foundation of China under Grants 61329301, 61374039, 61304010, 11301118, and 61573246, the Hujiang Foundation of China under Grants C14002
and D15009, the Alexander von Humboldt Foundation of Germany, and the Innovation Fund Project for Graduate Student of Shanghai under Grant JWCXSL140
A Bayesian Consistent Dual Ensemble Kalman Filter for State-Parameter Estimation in Subsurface Hydrology
Ensemble Kalman filtering (EnKF) is an efficient approach to addressing
uncertainties in subsurface groundwater models. The EnKF sequentially
integrates field data into simulation models to obtain a better
characterization of the model's state and parameters. These are generally
estimated following joint and dual filtering strategies, in which, at each
assimilation cycle, a forecast step by the model is followed by an update step
with incoming observations. The Joint-EnKF directly updates the augmented
state-parameter vector while the Dual-EnKF employs two separate filters, first
estimating the parameters and then estimating the state based on the updated
parameters. In this paper, we reverse the order of the forecast-update steps
following the one-step-ahead (OSA) smoothing formulation of the Bayesian
filtering problem, based on which we propose a new dual EnKF scheme, the
Dual-EnKF. Compared to the Dual-EnKF, this introduces a new update
step to the state in a fully consistent Bayesian framework, which is shown to
enhance the performance of the dual filtering approach without any significant
increase in the computational cost. Numerical experiments are conducted with a
two-dimensional synthetic groundwater aquifer model to assess the performance
and robustness of the proposed Dual-EnKF, and to evaluate its
results against those of the Joint- and Dual-EnKFs. The proposed scheme is able
to successfully recover both the hydraulic head and the aquifer conductivity,
further providing reliable estimates of their uncertainties. Compared with the
standard Joint- and Dual-EnKFs, the proposed scheme is found more robust to
different assimilation settings, such as the spatial and temporal distribution
of the observations, and the level of noise in the data. Based on our
experimental setups, it yields up to 25% more accurate state and parameters
estimates
Transform-based particle filtering for elliptic Bayesian inverse problems
We introduce optimal transport based resampling in adaptive SMC. We consider
elliptic inverse problems of inferring hydraulic conductivity from pressure
measurements. We consider two parametrizations of hydraulic conductivity: by
Gaussian random field, and by a set of scalar (non-)Gaussian distributed
parameters and Gaussian random fields. We show that for scalar parameters
optimal transport based SMC performs comparably to monomial based SMC but for
Gaussian high-dimensional random fields optimal transport based SMC outperforms
monomial based SMC. When comparing to ensemble Kalman inversion with mutation
(EKI), we observe that for Gaussian random fields, optimal transport based SMC
gives comparable or worse performance than EKI depending on the complexity of
the parametrization. For non-Gaussian distributed parameters optimal transport
based SMC outperforms EKI
An extended Kalman filtering approach to modeling nonlinear dynamic gene regulatory networks via short gene expression time series
Copyright [2009] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.In this paper, the extended Kalman filter (EKF) algorithm is applied to model the gene regulatory network from gene time series data. The gene regulatory network is considered as a nonlinear dynamic stochastic model that consists of the gene measurement equation and the gene regulation equation. After specifying the model structure, we apply the EKF algorithm for identifying both the model parameters and the actual value of gene expression levels. It is shown that the EKF algorithm is an online estimation algorithm that can identify a large number of parameters (including parameters of nonlinear functions) through iterative procedure by using a small number of observations. Four real-world gene expression data sets are employed to demonstrate the effectiveness of the EKF algorithm, and the obtained models are evaluated from the viewpoint of bioinformatics
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