Unscented Kalman Filtering of a Simulated pH System

Romanenko, Andrey; Santos, Lino O.; Afonso, Paulo

doi:10.1021/ie049899+

Cited by 59 publications

(26 citation statements)

References 31 publications

(49 reference statements)

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“…However, the Kalman filter adapts faster to weather change patterns, and there is certainly room for improvement on the version of the Kalman filter used in this study. Future studies should explore more complex forms of the Kalman filter, such as an ensemble Kalman filter (Gu and Oliver 2006), a dual Kalman filter (Aboy et al 2005), and an unscented Kalman filter (Romanenko et al 2004) to improve upon the current version of NWP model bias removal.…”

Section: Summary Remarksmentioning

confidence: 99%

Strengths and Weaknesses of MOS, Running-Mean Bias Removal, and Kalman Filter Techniques for Improving Model Forecasts over the Western United States

Cheng

Steenburgh

2007

Weather and Forecasting

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Despite improvements in numerical weather prediction, model errors, particularly near the surface, are unavoidable due to imperfect model physics, initial conditions, and boundary conditions. Here, three techniques for improving the accuracy of 2-m temperature, 2-m dewpoint, and 10-m wind forecasts by the Eta/North American Meso (NAM) Model are evaluated: (i) traditional model output statistics (ETAMOS), requiring a relatively long training period; (ii) the Kalman filter (ETAKF), requiring a relatively short initial training period (ϳ4-5 days); and (iii) 7-day running mean bias removal (ETA7DBR), requiring a 7-day training period. Forecasts based on the ETAKF and ETA7DBR methods were produced for more than 2000 MesoWest observing sites in the western United States. However, the evaluation presented in this study was based on subjective forecaster assessments and objective verification at 145 ETAMOS stations during summer 2004 and winter 2004/05. For the 145-site sample, ETAMOS produces the most accurate cumulative temperature, dewpoint, and wind speed and direction forecasts, followed by ETAKF and ETA7DBR, which have similar accuracy. Selected case studies illustrate that ETAMOS produces superior forecasts when model biases change dramatically, such as during large-scale pattern changes, but that ETAKF and ETA7DBR produce superior forecasts during quiescent cool season patterns when persistent valley and basin cold pools exist. During quiescent warm season patterns, the accuracy of all three methods is similar. Although the improved ETAKF cold pool forecasts are noteworthy, particularly since the Kalman filter can help better define cold pool structure by producing forecasts for locations without long-term records, alternative approaches are needed to improve forecasts during periods when model biases change dramatically.

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Section: Summary Remarksmentioning

confidence: 99%

Strengths and Weaknesses of MOS, Running-Mean Bias Removal, and Kalman Filter Techniques for Improving Model Forecasts over the Western United States

Cheng

Steenburgh

2007

Weather and Forecasting

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“…They elaborate at least two approaches for constructing EKF schemes, which can lead to a great variety of practical filtering algorithms based on ordinary or stochastic differential equation numerical methods. Thus, some criticism published on performance of the EKF in chemical systems for o ine models and industrial applications [6,12,33,35,36,39,40] does not mean that all other implementations of this method will also fail, as shown below.…”

Section: Dx(t) = F(x(t) U(t))dt + G(t)dw(t) T > 0 (11)mentioning

confidence: 99%

Practical implementation of extended Kalman filtering in chemical systems with sparse measurements

Kulikov

Kulikova

2018

Russian Journal of Numerical Analysis and Mathematical Modelling

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Chemical systems are often characterized by a number of peculiar properties that create serious challenges to state estimator algorithms. They may include hard nonlinear dynamics, states subject to some constraints arising from a physical nature of the process (for example, all chemical concentrations must be nonnegative), and so on. The classical Extended Kalman Filter (EKF), which is considered to be the most popular state estimator in practice, is shown to be ineffective in chemical systems with infrequent measurements. In this paper, we discuss a recently designed version of the EKF method, which is grounded in a high-order Ordinary Differential Equation (ODE) solver with automatic global error control. The implemented global error control boosts the quality of state estimation in chemical engineering and allows this newly built version of the EKF to be an accurate and efficient state estimator in chemical systems with both short and long waiting times (i.e., with frequent and infrequent measurements). So chemical systems with variable sampling periods are algorithmically admitted and can be treated as well.

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“…It uses a minimal set of determinate sample points (Sigma points) to completely assess the true mean and covariance of the states via UT. Studies show that UT is more accurate than linearization for propagating mean and covariance [7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Kalman Randomized Joint UKF Algorithm for Dual Estimation of States and Parameters in a Nonlinear System

Safarinejadian¹,

Vafamand²

2015

Journal of Electrical Engineering and Technology

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-This article presents a new nonlinear joint (state and parameter) estimation algorithm based on fusion of Kalman filter and randomized unscented Kalman filter (UKF), called Kalman randomized joint UKF (KR-JUKF). It is assumed that the measurement equation is linear. The KR-JUKF is suitable for time varying and severe nonlinear dynamics and does not have any systematic error. Finally, joint-EKF, dual-EKF, joint-UKF and KR-JUKF are applied to a CSTR with cooling jacket, in which production of propylene glycol happens and performance of KR-JUKF is evaluated.

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Unscented Kalman Filtering of a Simulated pH System

Cited by 59 publications

References 31 publications

Strengths and Weaknesses of MOS, Running-Mean Bias Removal, and Kalman Filter Techniques for Improving Model Forecasts over the Western United States

Strengths and Weaknesses of MOS, Running-Mean Bias Removal, and Kalman Filter Techniques for Improving Model Forecasts over the Western United States

Practical implementation of extended Kalman filtering in chemical systems with sparse measurements

Kalman Randomized Joint UKF Algorithm for Dual Estimation of States and Parameters in a Nonlinear System

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