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- W2603323269 abstract "The estimation of the states-parameters of non-linear system is often carried out usingExtended Kalman Filter (EKF). The EKF is only reliable for systems that are almost linear onthe time scale of the updates (i.e. sampling interval). The limitation of EKF can be overcomeby use of another class of recursive estimator named derivative free Kalman filter (DFKF) ormore popularly known as Unscented Kalman filter, a method that propagates mean andcovariance using non-linear transformation. In this paper two methods: i) factorized versionof EKF (UD Extended Kalman Filter or UDEKF) and ii) DFKF are studied and evaluatedusing various sets of simulated data of the non-linear systems as well as one real data set.Sensitivity study of DFKF with respect to tuning parameters such as α, β, and κ (used increation of sigma points and their associated weights) is also carried out using one set ofsimulated data. DFKF as compared to EKF is more accurate, easier to implement and hassame order of calculations. The concept of DFKF is extended to data fusion (DF) for similarsensors and algorithm is named DF-DFKF. Application of DFKF is demonstrated in parameterestimation problem." @default.
- W2603323269 created "2017-04-07" @default.
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- W2603323269 date "2008-05-01" @default.
- W2603323269 modified "2023-09-27" @default.
- W2603323269 title "EVALUATION OF DERIVATIVE FREE KALMAN FILTER FOR NON-LINEARSTATE-PARAMETER ESTIMATION AND FUSION" @default.
- W2603323269 hasPublicationYear "2008" @default.
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