Estimating model error covariance matrix parameters in extended Kalman filtering

Solonen, Antti; Hakkarainen, Janne; Ilin, Alexander; Abbas, Mudassar; Bibov, Alexander

doi:10.5194/npg-21-919-2014

Cited by 29 publications

(24 citation statements)

References 17 publications

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“…Note that in order to estimate the error covariance of the observations, several approaches are available to derive them directly from the observations [40,41]. These approaches provide fairly accurate estimate for the observation error covariance.…”

Section: Particle Filter (Pf) Algorithmmentioning

confidence: 99%

A robust dynamic state estimation for droop controlled islanded microgrids

Farag

Saxena

Asif

2016

Electric Power Systems Research

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Section: Particle Filter (Pf) Algorithmmentioning

confidence: 99%

A robust dynamic state estimation for droop controlled islanded microgrids

Farag

Saxena

Asif

2016

Electric Power Systems Research

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“…Recursive solution (29) can be efficiently evaluated by using the SMW formula for (C p k ) −1 like in Eq. (28), and writing C est…”

Section: Dimension Reduction Rauch-tung-striebel Smoothermentioning

confidence: 99%

Undersampled Dynamic X-Ray Tomography With Dimension Reduction Kalman Filter

Hakkarainen

Purisha

Solonen³

et al. 2019

IEEE Trans. Comput. Imaging

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In this paper, we consider prior-based dimension reduction Kalman filter for undersampled dynamic X-ray tomography. With this method, the X-ray reconstructions are parameterized by a low-dimensional basis. Thus, the proposed method is a) computationally very light; and b) extremely robust as all the computations can be done explicitly. With real and simulated measurement data, we show that the method provides accurate reconstructions even with very limited number of angular directions.

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“…The noise properties, namely, the noise CMs, have to be found on the basis of the measured data. Therefore, during the past 5 decades, various methods for the noise CM estimation have been proposed in the literature . The development of the noise CM estimation methods is thus closely tied with the advent of the SS models of dynamical systems being used in optimal and adaptive state estimation and control .…”

Section: Motivation Principal Classification and Goal Of The Papermentioning

confidence: 99%

“…The methods differ in assumptions related to the considered model, underlying ideas and principles, properties of the estimates, and number and essence of the design parameters. Traditionally, CM estimation methods are divided into 4 groups: the correlation methods , the maximum‐likelihood methods (MLMs) , the covariance matching methods (CMMs) , and the Bayesian methods …”

Section: Introductionmentioning

confidence: 99%

Noise covariance matrices in state‐space models: A survey and comparison of estimation methods—Part I

Duník

Straka

Kost

et al. 2017

Adaptive Control & Signal

118

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This paper deals with the estimation of the noise covariance matrices of systems described by state-space models. Stress is laid on the systematic survey and classification of both the recursive and batch processing methods proposed in the literature with a special focus on the correlation methods. Besides the correlation methods, representatives of other groups are introduced also with respect to their basic idea, estimate properties, assumptions and possible extensions, and user-defined parameters. Common and dual properties of the methods are highlighted, and a simulation comparison using exemplary MATLAB implementations of the methods is provided. KEYWORDSadaptive systems, noise covariance matrix, state estimation, state-space model, system identification INTRODUCTIONKnowledge of a system model is a key prerequisite for many state estimation, signal processing, fault detection, and optimal control problems. The model is often designed to be consistent with random behaviour of the system quantities and properties of the measurements. While the deterministic part of the model often arises from mathematical modelling on the basis of physical, chemical, or biological laws governing the behaviour of the system, the statistics of the stochastic part are often difficult to find by the modelling and have to be identified using the measured data. Incorrect description of the noise statistics may result in significant worsening of estimation, signal processing, detection, or control quality or even in a failure of the underlying algorithms.In the last 5 decades, therefore, a significant research interest has been focused on a design of the methods for the estimation of the properties of the stochastic part of the model. The attention has been devoted to both the input-output models 1-5 and the state-space (SS) models 6-14 and both recursive and batch processing methods. This paper focuses on the methods estimating the properties of the stochastic part of the system described by an SS model discrete in time. In particular, the methods estimating the covariance matrices (CMs) † of noises in the state and measurement equation from a sequence of measured data are of interest. The methods are further denoted as the noise CM estimation methods.

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Estimating model error covariance matrix parameters in extended Kalman filtering

Cited by 29 publications

References 17 publications

A robust dynamic state estimation for droop controlled islanded microgrids

A robust dynamic state estimation for droop controlled islanded microgrids

Undersampled Dynamic X-Ray Tomography With Dimension Reduction Kalman Filter

Noise covariance matrices in state‐space models: A survey and comparison of estimation methods—Part I

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