Extension of Friedland's separate-bias estimation to randomly time-varying bias of nonlinear systems

Zhou, Donghua; Y, Su; Yang, Xi; Zhang, Z.J.

doi:10.1109/9.233167

Cited by 76 publications

(35 citation statements)

References 10 publications

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“…For fast changing processes, a smaller ρ should be selected, and vice versa. As that pointed out in the paper (Zhou et al, 1993), (1 ) k λ + is insensitive to the value of ρ .…”

Section: Suboptimal Fading Extended Kalman Filtersupporting

confidence: 61%

Application of a Strong Tracking Finite-Difference Extended Kalman Filter to Eye Tracking

Zhang

2006

Lecture Notes in Computer Science

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“…For fast changing processes, a smaller ρ should be selected, and vice versa. As that pointed out in the paper (Zhou et al, 1993), (1 ) k λ + is insensitive to the value of ρ .…”

Section: Suboptimal Fading Extended Kalman Filtersupporting

confidence: 61%

Application of a Strong Tracking Finite-Difference Extended Kalman Filter to Eye Tracking

Zhang

2006

Lecture Notes in Computer Science

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“…Friedland (1969); Ignani (1990); Zhou et al (1993)). Because more assumptions must be imposed, it is in general not an ideal solution when little is known about the input variables.…”

Section: Gillins and De Moormentioning

confidence: 99%

State estimation with partially observed inputs: A unified Kalman filtering approach

2013

Automatica

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For linear stochastic time-varying state space models with Gaussian noises, this paper investigates state estimation for the scenario where the input variables of the state equation are not fully observed but rather the input data is available only at an aggregate level. Unlike the existing filters for unknown inputs that are based on the approach of minimum-variance unbiased estimation, this paper does not impose the unbiasedness condition for state estimation; instead it incorporates a Bayesian approach to derive a modified Kalman filter by pooling the prior knowledge about the state vector at the aggregate level with the measurements on the output variables at the original level of interest. The estimated state vector is shown to be a minimum-mean-square-error estimator. The developed filter provides a unified approach to state estimation: it includes the existing filters obtained under two extreme scenarios as its special cases, i.e., the classical Kalman filter where all the inputs are observed and the filter for unknown inputs.

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“…When the parameter bias fault happens, Equation (5) is compatible with the model and the AUKF could be used to estimate the characterizing parameters. The AUKF algorithm has strong robustness against uncertainties and disturbance and its robustness had been verified in [22].…”

Section: Joint Estimation Approachmentioning

confidence: 99%

“…STF uses a time-varying fading factor λ k+1 to adaptively adjust the state prediction covariance as follows [22]:…”

Section: Joint Estimation Approachmentioning

confidence: 99%

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Diagnosis Method for Li-Ion Battery Fault Based on an Adaptive Unscented Kalman Filter

Zheng

Chen

et al. 2017

Energies

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Abstract:The reliability of battery fault diagnosis depends on an accurate estimation of the state of charge and battery characterizing parameters. This paper presents a fault diagnosis method based on an adaptive unscented Kalman filter to diagnose the parameter bias faults for a Li-ion battery in real time. The first-order equivalent circuit model and relationship between the open circuit voltage and state of charge are established to describe the characteristics of the Li-ion battery. The parameters in the equivalent circuit model are treated as system state variables to set up a joint state and parameter space equation. The algorithm for fault diagnosis is designed according to the estimated parameters. Two types of fault of the Li-ion battery, including internal ohmic resistance fault and diffusion resistance faults, are studied as a case to validate the effectiveness of the algorithm. The experimental results show that the proposed approach in this paper has effective tracking ability, better estimation accuracy, and reliable diagnosis for Li-ion batteries.

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Extension of Friedland's separate-bias estimation to randomly time-varying bias of nonlinear systems

Cited by 76 publications

References 10 publications

Application of a Strong Tracking Finite-Difference Extended Kalman Filter to Eye Tracking

Application of a Strong Tracking Finite-Difference Extended Kalman Filter to Eye Tracking

State estimation with partially observed inputs: A unified Kalman filtering approach

Diagnosis Method for Li-Ion Battery Fault Based on an Adaptive Unscented Kalman Filter

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