A quantified approach of predicting suitability of using the unscented Kalman filter in a non-linear application. (English) Zbl 1451.93386
Summary: A mathematical framework to predict the unscented Kalman filter (UKF) performance improvement relative to the extended Kalman filter (EKF) using a quantitative measure of non-linearity is presented. It is also shown that the range of performance improvement the UKF can attain, for a given minimum probability depends on the nonlinearity indices of the corresponding system and measurement models. Three distinct non-linear estimation problems are examined to verify these relations. A launch vehicle trajectory estimation problem, a satellite orbit estimation problem and a re-entry vehicle position estimation problem are examined to verify these relations. Using these relations, a procedure is suggested to predict the estimation performance improvement offered by the UKF relative to the EKF for a given nonlinear system and measurement without designing, implementing and tuning the two Kalman filters.
Keywords:
estimation theory; nonlinear observer and filter design; tracking; extended Kalman filter; unscented Kalman filter; nonlinearityReferences:
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