Experimental application of extended Kalman filtering for sensor validation

Gobbo, D. Del; Napolitano, Marcello R.; Famouri, Parviz; Innocenti, Mario

doi:10.1109/87.911389

Cited by 80 publications

(44 citation statements)

References 5 publications

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“…For other time segments without process model uncertainty, the MSTUKF is carried out optimally with (5). Therefore, the MSTUKF can avoid the loss of precision in the time segments without process model uncertainty, which is a problem for both STF and STUKF.…”

Section: Identification Of Process Model Uncertaintymentioning

confidence: 99%

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Modified strong tracking unscented Kalman filter for nonlinear state estimation with process model uncertainty

Gao

Zhong

et al. 2015

Adaptive Control & Signal

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This paper presents a modified strong tracking unscented Kalman filter (MSTUKF) to address the performance degradation and divergence of the unscented Kalman filter because of process model uncertainty. The MSTUKF adopts the hypothesis testing method to identify process model uncertainty and further introduces a defined suboptimal fading factor into the prediction covariance to decrease the weight of the prior knowledge on filtering solution. The MSTUKF not only corrects the state estimation in the occurrence of process model uncertainty but also avoids the loss of precision for the state estimation in the absence of process model uncertainty. Further, it does not require the cumbersome evaluation of Jacobian matrix involved in the calculation of the suboptimal fading factor. Experimental results and comparison analysis demonstrate the effectiveness of the proposed MSTUKF.

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Section: Identification Of Process Model Uncertaintymentioning

confidence: 99%

“…Accordingly, various approaches have been studied, including the extended Kalman filter (EKF) [4,5], Gaussian sum filter [6], Gauss-Hermite filter [7,8], unscented Kalman filter (UKF) [1,9,10] and particle filter [11,12]. These methods are mainly derived based on the framework of the basic Kalman filter (KF).…”

Section: Introductionmentioning

confidence: 99%

Modified strong tracking unscented Kalman filter for nonlinear state estimation with process model uncertainty

Gao

Zhong

et al. 2015

Adaptive Control & Signal

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“…The resulting filter is referred to as an augmented filter. This FDI approach has proven its usefulness in Gobbo et al (2001) in which an extended Kalman filter, which linearizes the nonlinear model each time step, is applied to FDI. This linearization at each time step can be very time inefficient for large systems.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection and Identification of Actuator Faults Using Linear Parameter Varying Models

Hallouzi

Verdult

Babuška

et al. 2005

IFAC Proceedings Volumes

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A method is proposed to detect and identify two common classes of actuator faults in nonlinear systems. The two fault classes are total and partial actuator faults. This is accomplished by representing the nonlinear system by a Linear Parameter Varying (LPV) model, which is derived from experimental input-output data. The LPV model is used in a Kalman filter to estimate augmented states, which are directly related to the faults. Decision logic has been developed to determine the fault class from the estimated augmented states. The proposed method has been validated on a nonlinear simulation model of a small commercial aircraft.

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“…Some nonlinear filters can preserve the shapes, edges and amplitudes of the signal at the same time as filtering out the noise [4,5]. Anisotropic diffusion is one of them.…”

Section: Introductionmentioning

confidence: 99%

Anisotropic median-diffusion for filtering noisy electrocardiogram signals

Melo¹,

Kim

Nicolosi

2008

2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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-Low Noise Electrocardiogram (ECG) has been widely used for heart disease diagnosis. The anisotropic median-diffusion is the filter obtained by intercalating a median filtering in each diffusion step. We propose to use anisotropic median-diffusion to filter noisy ECG signals. We describe how to estimate appropriate parameters of the proposed filter. We validate our method using ECG signals from the MIT-BIH databases (many of them with premature ventricular contraction) and compare our method with other filtering methods. Experiments show that the proposed technique can effectively remove the noise without changing the instants and amplitudes of events, as well as preserving the morphologies of ECG signals in sections of the QRS complex.

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Experimental application of extended Kalman filtering for sensor validation

Cited by 80 publications

References 5 publications

Modified strong tracking unscented Kalman filter for nonlinear state estimation with process model uncertainty

Modified strong tracking unscented Kalman filter for nonlinear state estimation with process model uncertainty

Fault Detection and Identification of Actuator Faults Using Linear Parameter Varying Models

Anisotropic median-diffusion for filtering noisy electrocardiogram signals

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