Abstract
The paper deals with state estimation of nonlinear stochastic dynamic systems. Various unscented Kalman filter (UKF) algorithms are analyzed with the focus on computation and transformation of the σ-points for the purpose of update of state estimate moments. An algorithm of the pure propagation UKF is developed transforming the initial σ-point set forward in time without necessity of its recomputation as is usual in classical UKF algorithms. Such direct transformation of the σ-points keeps higher order moments of the σ-point set and leads consequently to an increased accuracy of the state estimate. The proposed pure propagation unscented Kalman filter is illustrated in a numerical example.
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