EVALUATION OF DERIVATIVE FREE KALMAN FILTER FOR NON-LINEARSTATE-PARAMETER ESTIMATION AND FUSION
2008
The estimation of the states-parameters of non-linear system is often carried out using
Extended Kalman Filter (EKF). The EKF is only reliable for systems that are almost linear on
the time scale of the updates (i.e. sampling interval). The limitation of EKF can be overcome
by use of another class of recursive estimator named derivative free Kalman filter (DFKF) or
more popularly known as Unscented Kalman filter, a method that propagates mean and
covariance using non-linear transformation. In this paper two methods: 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 as well as one real data set.
Sensitivity study of DFKF with respect to tuning parameters such as α, β, and κ (used in
creation of sigma points and their associated weights) is also carried out using one set of
simulated data. DFKF as compared to EKF is more accurate, easier to implement and has
same order of calculations. The concept of DFKF is extended to data fusion (DF) for similar
sensors and algorithm is named DF-DFKF. Application of DFKF is demonstrated in parameter
estimation problem.
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