Dynamic State Estimation in the Presence of Sensor Outliers Using MAP-Based EKF

Chughtai, Aamir Hussain; Akram, Usman; Tahir, Muhammad; Uppal, Momin

doi:10.1109/lsens.2020.2983453

Cited by 12 publications

(9 citation statements)

References 19 publications

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“…q(ψ k ) denotes the expectation of the argument with respect to a distribution q(ψ k ). The VB marginals can be updated iteratively until convergence, using ( 8)- (10) in turn.…”

Section: Recursive Bayesian Inferencementioning

confidence: 99%

“…For the update step, we resort to (6)- (10) and use (13) for approximating the predictive density. For detailed derivations, the reader is referred to the Appendix.…”

Section: B Updatementioning

confidence: 99%

“…Using (10) and (13) we can write q(Θ k ) as q(Θ k ) ∝ exp ln(p(y k |x k , I k , Θ k )p(I k )p(x k |y 1:k-1 )…”

Section: A Predicting Parameters Ofmentioning

confidence: 99%

“…Random errors -also known as outliers -behave as zero-mean noise, causing sudden sporadic surges in the measurements. Their occurrence can be attributed to factors like sensor degradation, communication failures, environmental influences, etc [10]. On the other hand, gross errors -also known as biases -manifest in the data statistically as nonzeromean noise.…”

Section: Introductionmentioning

confidence: 99%

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Variational-Based Non-linear Bayesian Filtering with Observations Subjected to Gross Errors

Chughtai¹,

Majal²,

Tahir³

et al. 2021

Preprint

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State estimation of dynamical systems is crucial for providing new decision-making and system automation information in different applications. However, the assumptions on the standard computational models for sensor measurements can be violated in practice due to different types of data abnormalities such as random and gross errors. In this work, we focus on the occurrence of gross errors and propose a robust filter for their detection and mitigation during state estimation of nonlinear dynamical systems. We model the presence of gross errors within the generative structure of the state-space models. Subsequently, employing the theory of Variational Bayes and general Gaussian filtering, we devise a recursive filter which we call the Bias Detecting and Mitigating (BDM) filter. As the error detection mechanism is embedded within the filter structure its dependence on any external detector is obviated. Simulations verify the performance gains of the proposed BDM filter compared to similar Kalman filtering-based approaches in terms of robustness to temporary and persistent bias presence.

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“…q(ψ k ) denotes the expectation of the argument with respect to a distribution q(ψ k ). The VB marginals can be updated iteratively until convergence, using ( 8)- (10) in turn.…”

Section: Recursive Bayesian Inferencementioning

confidence: 99%

“…For the update step, we resort to (6)- (10) and use (13) for approximating the predictive density. For detailed derivations, the reader is referred to the Appendix.…”

Section: B Updatementioning

confidence: 99%

“…Using (10) and (13) we can write q(Θ k ) as q(Θ k ) ∝ exp ln(p(y k |x k , I k , Θ k )p(I k )p(x k |y 1:k-1 )…”

Section: A Predicting Parameters Ofmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Variational-Based Non-linear Bayesian Filtering with Observations Subjected to Gross Errors

Chughtai¹,

Majal²,

Tahir³

et al. 2021

Preprint

Self Cite

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show abstract

“…Using VB inference and general Gaussian filtering, we devise an outlier-robust learning-based filter for nonlinear SSMs, which discards only the corrupted measurements during inference. Unlike the method in [25], we do not take any restrictive assumption that outliers can occur in at most one measurement dimension at any given instant. By comparing our method with other learningbased tractable approaches in the simulations we verify the performance gains.…”

Section: Introductionmentioning

confidence: 99%

Outlier-Robust Filtering For Nonlinear Systems With Selective Observations Rejection

Chughtai¹,

Tahir²,

Uppal³

2021

Preprint

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This letter presents a novel outlier-robust filter for nonlinear dynamical systems. We consider a common case where measurements are obtained from independent sensors. The proposed method is devised by modifying the measurement model and subsequently using the theory of Variational Bayes and general Gaussian filtering. We treat the measurement outliers independently for independent observations leading to selective rejection of the corrupted observations during inference. By carrying out simulations for variable number of sensors we verify that an implementation of the proposed filter is computationally more efficient as compared to similar baseline methods.

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A new robust dynamic state estimation approach for power systems with non-Gaussian noise

Chen,

Liu,

Luo

et al. 2024

International Journal of Electrical Power & Energy Systems

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Dynamic State Estimation in the Presence of Sensor Outliers Using MAP-Based EKF

Cited by 12 publications

References 19 publications

Variational-Based Non-linear Bayesian Filtering with Observations Subjected to Gross Errors

Variational-Based Non-linear Bayesian Filtering with Observations Subjected to Gross Errors

Outlier-Robust Filtering For Nonlinear Systems With Selective Observations Rejection

A new robust dynamic state estimation approach for power systems with non-Gaussian noise

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