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Evaluation of Derivative Free Kalman Filter and Fusion in Non-Linear Estimation

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2 Author(s)
Kashyap, S.K. ; Flight Mech. & Control Div., Nat. Aerosp. Lab., Bangalore ; Raol, J.R.

In recent literature a derivative free Kalman filter (DFKF) a method that propagates mean and covariance using nonlinear transformation is frequently used. In this paper i) factorized version of EKF (UD extended Kalman filter or UDEKF) and ii) DFKF are studied and evaluated using various sets of simulated data of the non-linear systems. Sensitivity study of DFKF with respect to tuning parameters used in creation of sigma points and the associated weights is carried out. DFKF is more accurate and easier to implement. A data fusion scheme is evolved and presented based on DFKF for similar sensors. Its performance is evaluated. It is observed that fusion enhances the estimation accuracy of the state of non-linear plant. Application of DFKF to non-linear parameter estimation problem is also demonstrated

Published in:

Electrical and Computer Engineering, 2006. CCECE '06. Canadian Conference on

Date of Conference:

May 2006