Fuzzy adaptive Kalman filtering for INS/GPS data fusion

Sąsiadek, Jurek Z.; Wang, Q.; Zeremba, M.B.

doi:10.1109/isic.2000.882920

Cited by 109 publications

(50 citation statements)

References 4 publications

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“…X n(-)=F(n) X n_ l (+) (10) � (-) = F(n)� _ l (+ )FT (n)+ Q (n) (11) S(n)=H(n)�(-)HT(n)+R(n) (12) Kn =�(-)HT(n)[S(n)r l (13) r(n)=z(n)-H(n)xn(-) (14) X n(+)=xn(-)+KJr(n)] (15) � ( + ) = (I -KnH (n ))� (-)…”

Section: A Kalman Filterunclassified

The application of fuzzy adaptive Kalman filtering and smoother Kalman for well logging tool

Soltani

Karimaghaee

2013

Iccke 2013

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In this article, the application of fuzzy adaptive Kalman filter for estimation of well-log data problems is presented The adaptive technics has been an approach to avoid the divergence problem in Kalman filter when the detailed knowledge about a system models are not completed Natural gamma-ray tool (NGT) is a natural gamma ray sonde, which detects the emitted gamma-ray during the formation of thorium, uranium and potassium at different energy levels. A computer is used to measure the concentrations of Th (Thorium), U (Uranium) and K (potassium) in different depths. Regular techniques have some uncertainties in the estimation of reservoir evaluation, regarding to the perturbation. In this paper, it is shown that there may be an adaptive Kalman filter with the fuzzy inference system and Rauch-Tung-Striebel smoother, based on a model with less information, has a better estimation of the concentrations of Th, U and K in vertical variations.

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Section: A Kalman Filterunclassified

The application of fuzzy adaptive Kalman filtering and smoother Kalman for well logging tool

Soltani

Karimaghaee

2013

Iccke 2013

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“…Thus, it is concluded that EKF algorithm necessitates those covariance matrices to be adapted online according to the different operational conditions of IM. Interested readers can refer to [12][13][14][15][16] for getting an idea about the research areas that concern with the tuning of KF, adaptively.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy based parameter tuning of EKF observers for sensorless control of Induction Motors

Aydin

Gökaşan

Boğosyan

2014

2014 International Symposium on Power Electronics, Electrical Drives, Automation and Motion

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This study presents a parameter tuning approach for Extended Kalman Filter (EKF) based observers for the sensorless control of Induction Motor (IM) drives. After an analysis performed on the effect of covariance matrix elements of EKF, the study demonstrates the improved performance of the EKF based estimation (performed for stator currents, rotor flux, rotor speed, stator resistance and load torque), via the developed online parameter tuning approach for different speed and load references. Firstly, it has been demonstrated experimentally that covariance matrices used in EKF algorithm vary with the operation conditions. It has specifically been demonstrated that, among the elements of model covariance matrix, the ones corresponding to the rotor flux components are the most effective in correcting the estimations of the related EKF algorithm. To address this issue, an online fuzzy approach is developed based on different load and speed references, of which the inputs are the estimated speed and estimated load torque, and the output consists of the elements of the model covariance matrix related to the rotor flux. The performance of the proposed Fuzzy EKF has been experimentally tested and the results have demonstrated that the proposed scheme can eliminate biases and yields higher estimation accuracy when compared with the standard EKF where the tuning parameters are fixed to constant values.

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“…To resolve the shortcoming, a new approach called the fuzzy strong tracking unscented Kalman filter (FSTUKF) is suggested. The fuzzy logic reasoning system (Sasiadek, et al 2000) based on the Takagi-Sugeno (T-S) model (Takagi and Sugeno 1985) is incorporated into the STUKF. The fuzzy reasoning system is constructed to obtain suitable softening factors according to the time-varying change in dynamics.…”

Section: Introduction the Globalmentioning

confidence: 99%

Navigation Integration Using the Fuzzy Strong Tracking Unscented Kalman Filter

Jwo

Lai

2009

J. Navigation

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A navigation integration processing scheme, called the strong tracking unscented Kalman filter (STUKF), is based on the combination of an unscented Kalman filter (UKF) and a strong tracking filter (STF). The UKF employs a set of sigma points by deterministic sampling, such that the linearization process is not necessary, and therefore the error caused by linearization as in the traditional extended Kalman filter (EKF) can be avoided. As a type of adaptive filter, the STF is essentially a nonlinear smoother algorithm that employs suboptimal multiple fading factors, in which the softening factors are involved. In order to resolve the shortcoming in traditional approach for selecting the softening factor through personal experience or computer simulation, a novel scheme called the fuzzy strong tracking unscented Kalman filter (FSTUKF) is presented where the Fuzzy Logic Adaptive System (FLAS) is incorporated for determining the softening factor. The proposed FSTUKF algorithm shows promising results in estimation accuracy when applied to the integrated navigation system design, as compared to the EKF, UKF and STUKF approaches.1. Integrated navigation. 2. Unscented Kalman filter. 3. Strong tracking filter. 4. Fuzzy logic.

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Fuzzy adaptive Kalman filtering for INS/GPS data fusion

Cited by 109 publications

References 4 publications

The application of fuzzy adaptive Kalman filtering and smoother Kalman for well logging tool

The application of fuzzy adaptive Kalman filtering and smoother Kalman for well logging tool

Fuzzy based parameter tuning of EKF observers for sensorless control of Induction Motors

Navigation Integration Using the Fuzzy Strong Tracking Unscented Kalman Filter

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