Adaptive estimation of noise covariance matrices in real-time preprocessing of geophysical data

Noriega, Gerardo; Pasupathy, S.

doi:10.1109/36.628782

Cited by 33 publications

(24 citation statements)

References 27 publications

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“…In the early 1970s, Mehra [5] classified into four categories the methods for the estimation of the KF covariances: Bayesian [6], [7], ML [8]- [10], correlation [11]- [20], and covariance matching [2], [3].…”

Section: Literature Reviewmentioning

confidence: 99%

A Prediction-Error Covariance Estimator for Adaptive Kalman Filtering in Step-Varying Processes: Application to Power-System State Estimation

Zanni

Boudec

Cherkaoui

et al. 2017

IEEE Trans. Contr. Syst. Technol.

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Abstract-In this paper, we present a new method for the estimation of the prediction-error covariances of a Kalman filter (KF), which is suitable for step-varying processes. The method uses a series of past innovations (i.e., the difference between the upcoming measurement set and the KF predicted state) to estimate the prediction-error covariance matrix by means of a constrained convex optimization problem. The latter is designed to ensure the symmetry and the positive semidefiniteness of the estimated covariance matrix, so that the KF numerical stability is guaranteed. Our proposed method is straightforward to implement and requires the setting of one parameter only, i.e., the number of past innovations to be considered. It relies on the knowledge of a linear and stationary measurement model. The ability of the method to track state step-variations is validated in ideal conditions for a random-walk process model and for the case of power-system state estimation. The proposed approach is also compared with other methods that estimate the KF stochastic parameters and with the well-known linear weighted least squares. The comparison is given in terms of both accuracy and computational time.Index Terms-Adaptive Kalman filter (AKF), covariance estimation, phasor measurement unit (PMU), power systems, state estimation, step processes.

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Section: Literature Reviewmentioning

confidence: 99%

A Prediction-Error Covariance Estimator for Adaptive Kalman Filtering in Step-Varying Processes: Application to Power-System State Estimation

Zanni

Boudec

Cherkaoui

et al. 2017

IEEE Trans. Contr. Syst. Technol.

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show abstract

“…This convex program can be solved efficiently with Newton's method and the gradient can be determined analytically [1]. This approach is a significant simplification of the strategy to maintain positive semi-definiteness proposed by [17]. None of the earlier approaches have been tested on closed-loop data coming from controllers containing integrated white noise disturbance models.…”

Section: Comments On Previous Workmentioning

confidence: 99%

The autocovariance least-squares method for estimating covariances: application to model-based control of chemical reactors

Odelson¹,

Lutz

Rawlings

2006

IEEE Trans. Contr. Syst. Technol.

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Abstract-This paper demonstrates the autocovariance least-squares (ALS) technique on two chemical reactor control problems. The method uses closed-loop process data to recover the covariances of the disturbances entering the process, which are required for state estimation. The data used for this purpose may be collected with or without the controllers running. We do not assume that the plant is at steady state nor that only nonzero disturbances are affecting the plant at the time of data collection. The ALS method also accounts for integrated white noise disturbances, which are required for offset-free control. Two examples are provided in this paper: a carefully controlled laboratory reactor and an industrial reactor controlled by a state-of-the-art advanced model predictive control (MPC) system. A variety of control scenarios are tested and the results demonstrate that the ALS method has the potential to improve the best industrial practice of process control by a factor of three to five.

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“…Loebis et al (Loebis et al, 2004) proposed an adaptive EKF algorithm, which adjusted the measurement noise covariance matrix by fuzzy logic. Other works can also be seen in references (Noriega & Pasupathy, 1997;Mehra, 1970;Hu et al, 2003;Chaer et al, 1997;Garcia-Velo, 1997). As far as the adaptive UKF (AUKF) is concerned, the most-oftenmentioned scheme was proposed by Lee and Alfriend (Lee & Alfriend, 2004), where the Maybeck's method (Maybeck, 1979) was modified by maximum-likelihood principle to estimate the error covariance matrix, and this estimator was further integrated into the normal UKF as the adaptive mechanism.…”

mentioning

confidence: 94%

Adaptive Unscented Kalman Filter and Its Applications in Nonlinear Control

Han¹,

Song²,

He³

2009

Kalman Filter Recent Advances and Applications

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Adaptive estimation of noise covariance matrices in real-time preprocessing of geophysical data

Cited by 33 publications

References 27 publications

A Prediction-Error Covariance Estimator for Adaptive Kalman Filtering in Step-Varying Processes: Application to Power-System State Estimation

A Prediction-Error Covariance Estimator for Adaptive Kalman Filtering in Step-Varying Processes: Application to Power-System State Estimation

The autocovariance least-squares method for estimating covariances: application to model-based control of chemical reactors

Adaptive Unscented Kalman Filter and Its Applications in Nonlinear Control

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