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A Knowledge-Aided Robust Ensemble Kalman Filter Algorithm for Non-Linear and Non-Gaussian Large Systems

Santiago Lopez-Restrepo, Andres Yarce, Nicolás Pinel, O. L. Quintero, Arjo Segers, A. W. Heemink
2022 Frontiers in Applied Mathematics and Statistics  
and non-Gaussian large systems.  ...  This work proposes a robust and non-Gaussian version of the shrinkage-based knowledge-aided EnKF implementation called Ensemble Time Local H∞ Filter Knowledge-Aided (EnTLHF-KA).  ...  The Ensemble Kalman filter (EnKF) is a KF-based Monte Carlo approximation of the KF when the state space is large, and the model is non-linear [4] .  ... 
doi:10.3389/fams.2022.830116 fatcat:jvxpn3jgpfaafndzxebt5ormya

State-of-the-art stochastic data assimilation methods for high-dimensional non-Gaussian problems

Sanita Vetra-Carvalho, Peter Jan van Leeuwen, Lars Nerger, Alexander Barth, M. Umer Altaf, Pierre Brasseur, Paul Kirchgessner, Jean-Marie Beckers
2018 Tellus: Series A, Dynamic Meteorology and Oceanography  
Most variants of Ensemble Kalman Filters, Particle Filters and second-order exact methods are discussed, including Gaussian Mixture Filters, while methods that require an adjoint model or a tangent linear  ...  Thus, the research in non-linear DA Tellus A: 2018.  ...  Acknowledgements PJvL thanks the European Research Council (ERC) for funding of the CUNDA project under the European Unions Horizon 2020 research and innovation programme.  ... 
doi:10.1080/16000870.2018.1445364 fatcat:ercqis2ohrfujjfrmp3py4vb6m

Multifidelity Ensemble Kalman Filtering Using Surrogate Models Defined by Theory-Guided Autoencoders

Andrey A. Popov, Adrian Sandu
2022 Frontiers in Applied Mathematics and Statistics  
The multifidelity ensemble Kalman filter (MFEnKF) recently developed by the authors combines a full-order physical model and a hierarchy of reduced order surrogate models in order to increase the computational  ...  Numerical experiments with the canonical Lorenz'96 model illustrate that nonlinear surrogates perform better than linear projection-based ones in the context of multifidelity ensemble Kalman filtering.  ...  ACKNOWLEDGMENTS The authors would like to thank the rest of the members from the Computational Science Laboratory at Virginia Tech, and Traian Iliescu from the Mathematics Department at Virginia Tech.  ... 
doi:10.3389/fams.2022.904687 fatcat:oz7palemsbcwfdiv3f73t5cr34

Errors in Ensemble Kalman Smoother Estimates of Cloud Microphysical Parameters

Derek J. Posselt, Daniel Hodyss, Craig H. Bishop
2014 Monthly Weather Review  
The posterior distribution of analysis errors obtained from ensemble Kalman filters and smoothers is independent of observed values.  ...  If forecast or observation error distributions are non-Gaussian, the true posterior mean and covariance depends on the distribution of observation errors and the observed values.  ...  Quadratic ensemble smoother The quadratic ensemble filter (QEF; Hodyss 2011) extends the linear regression capability of the Kalman filter resulting in an algorithm that performs nonlinear polynomial regression  ... 
doi:10.1175/mwr-d-13-00290.1 fatcat:evyy7fmvwffmphxxejpatjvny4

An efficient ensemble Kalman Filter implementation via shrinkage covariance matrix estimation: exploiting prior knowledge

Santiago Lopez-Restrepo, Elias D. Nino-Ruiz, Luis G. Guzman-Reyes, Andres Yarce, O. L. Quintero, Nicolas Pinel, Arjo Segers, A. W. Heemink
2021 Computational Geosciences  
Our filter implementation combines information brought by an ensemble of model realizations, and that based on our prior knowledge about the dynamical system of interest.  ...  AbstractIn this paper, we propose an efficient and practical implementation of the ensemble Kalman filter via shrinkage covariance matrix estimation.  ...  The linear and Gaussian case is solved by the well known Kalman filter, and its extension to non-linear and Gaussian cases can be found extensively in the literature.  ... 
doi:10.1007/s10596-021-10035-4 fatcat:dgdpokcw5fdslgm2fb3ha62nwu

A Review of Innovation-Based Methods to Jointly Estimate Model and Observation Error Covariance Matrices in Ensemble Data Assimilation [article]

Pierre Tandeo, Pierre Ailliot, Marc Bocquet, Alberto Carrassi, Takemasa Miyoshi, Manuel Pulido, Yicun Zhen
2020 arXiv   pre-print
This review aims to present and to discuss, with a unified framework, different methods to jointly estimate the Q and R matrices using ensemble-based data assimilation techniques.  ...  Most of the current data assimilation algorithms consider the model and observation error terms as additive Gaussian noise, specified by their covariance matrices Q and R, respectively.  ...  Wells, Gilles-Olivier Guégan and Aimée Johansen for their English grammar corrections. We also thank the five anonymous reviewers for their precious comments and ideas to improve this review paper.  ... 
arXiv:1807.11221v5 fatcat:y3m6f4r5avakfjuta4nc5yucxy

Robust Model-Free Software Sensors for the HIV/AIDS Infection Process

Hussain Alazki, Alexander Poznyak
2017 International Journal of Modern Nonlinear Theory and Application  
So, here we deal with an uncertain dynamic model that excludes the application of classical filtering theory and requires the application of robust filters successfully working in the absence of a complete  ...  This paper considers the problem of the HIV/AIDS Infection Process filtering characterized by three compounds, namely, the number of healthy T-cells, the number of infected T-cells and free virus particles  ...  This gave rise to a class of algorithm called the extended Kalman filter [9] .  ... 
doi:10.4236/ijmnta.2017.62004 fatcat:t5yk7mwnibebnngzswyx2k6e6y

Kalman Filter: Historical Overview and Review of Its Use in Robotics 60 Years after Its Creation

Claudio Urrea, Rayko Agramonte, Giovanni Diraco
2021 Journal of Sensors  
This work reviews some of the modifications conducted on to this algorithm over the last years. Problems such as the consistency, convergence, and accuracy of the filter are also dealt with.  ...  Sixty years after its creation, the Kalman filter is still used in autonomous navigation processes, robot control, and trajectory tracking, among other activities.  ...  In addition, the particle filter adapts better to the situation when system noise is non-Gaussian.  ... 
doi:10.1155/2021/9674015 fatcat:jpbjftwvjbcfzcr4i3j32epdru

Piecewise Model based Online Prognosis of Lithium-Ion Batteries using Particle Filters

Karkulali Pugalenthi, Hyunseok Park, Nagarajan Raghavan
2020 IEEE Access  
Lithium-ion batteries are used as energy sources for energy storage systems, electric vehicles, consumer electronic devices and much more.  ...  We use prediction error and execution time as the prognostic metrics for comparison.  ...  Particle filters (PF) are extensively used for the purpose of prognosis because of its ability to handle non-linear systems with non-Gaussian noise.  ... 
doi:10.1109/access.2020.3017810 fatcat:uqdxfdjnwjhy3j4ismg35whxy4

Pseudo-Orbit Data Assimilation. Part I: The Perfect Model Scenario

Hailiang Du, Leonard A. Smith
2014 Journal of the Atmospheric Sciences  
In this setting, ensemble Kalman filter approaches are hampered by their foundational assumptions of dynamical linearity, while variational approaches may fail in practice owing to local minima in their  ...  Empirical results demonstrate improved performance over that of the two most common traditional approaches of data assimilation (ensemble Kalman filter and four-dimensional variational assimilation). 1  ...  An approximation to the Bayesian approach, called the Kalman filter, introduced by Kalman (1960) , is optimal only for linear models and a Gaussian observational noise.  ... 
doi:10.1175/jas-d-13-032.1 fatcat:ros5z5orynbd7lzk4uolb42iuy

Cooperative Robot Localization Using Event-triggered Estimation [article]

Michael Ouimet, David Iglesias, Nisar Ahmed, Sonia Martinez
2018 arXiv   pre-print
This paper describes a novel communication-spare cooperative localization algorithm for a team of mobile unmanned robotic vehicles.  ...  Since agents know the event-triggering condition for measurements to be sent, the lack of a measurement is thus also informative and fused into state estimates.  ...  Acknowledgments David Iglesias was supported by a fellowship from the Balsells Fellowship Program.  ... 
arXiv:1802.07346v1 fatcat:6dlxonem7bh3tia7ev2mju6d64

A UKF-PF based Hybrid Estimation Scheme for Space Object Tracking [article]

Dilshad Raihan A.V., Suman Chakravorty
2014 arXiv   pre-print
In this paper, we present a UKF-PF based hybrid nonlinear filter for space object tracking.  ...  The proposed nonlinear filter employs an unscented Kalman filter (UKF) estimate the state of the system while measurements are available.  ...  The Kalman filter furnishes the unbiased minimum variance estimates when the dynamical system is linear and the uncertainties involved are Gaussian.  ... 
arXiv:1409.7723v1 fatcat:gpihivqmfrfjzlyyfplrfsmnta

Context-Aided Sensor Fusion for Enhanced Urban Navigation

Enrique Martí, David Martín, Jesús García, Arturo de la Escalera, José Molina, José Armingol
2012 Sensors  
This article details an advanced GNSS/IMU fusion system based on a context-aided Unscented Kalman filter for navigation in urban conditions.  ...  The constrained non-linear filter is here conditioned by a contextual knowledge module which reasons about sensor quality and driving context in order to adapt it to the situation, while at the same time  ...  Acknowledgments This work was supported in part by Projects CICYT TIN2011-28620-C02-01, CICYT TEC2011-28626-C02-02, CAM CONTEXTS (S2009/TIC-1485), CICYT TRA2010-20255-C03-01, CICYT TRA2011-29454-C03-02 and  ... 
doi:10.3390/s121216802 pmid:23223080 pmcid:PMC3571812 fatcat:b5f7faedqzdirkzsx57puczpxu

2020 Index IEEE Signal Processing Letters Vol. 27

2020 IEEE Signal Processing Letters  
., +, LSP 2020 995-999 A New Robust Kalman Filter With Adaptive Estimate of Time-Varying Mea- surement Bias.  ...  ., +, LSP 2020 1000-1004 A Sparse Robust Adaptive Filtering Algorithm Based on theq-Rényi Kernel Function.  ... 
doi:10.1109/lsp.2021.3055468 fatcat:wfdtkv6fmngihjdqultujzv4by

State Estimators in Soft Sensing and Sensor Fusion for Sustainable Manufacturing

Marion McAfee, Mandana Kariminejad, Albert Weinert, Saif Huq, Johannes D. Stigter, David Tormey
2022 Sustainability  
State estimators, including observers and Bayesian filters, are a class of model-based algorithms for estimating variables in a dynamical system given the sensor measurements of related system states.  ...  We discuss current and emerging trends in using state estimation as a framework for combining physical knowledge with other sources of data for monitoring and controlling distributed manufacturing systems  ...  Suitable for linear system 2. Not suitable for non-Gaussian noise 3. Not suitable for high order systems Extended Kalman Filter 1. Suitable for noisy systems 2.  ... 
doi:10.3390/su14063635 fatcat:3a2twj3nlzbszlhc5ijluvekde
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