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The normal unscented Kalman filter (UKF) suffers from performance degradation and even divergence while mismatch between the noise distribution assumed to be known as a priori by UKF and the true ones in a real system. In this paper, a novel adaptive UKF (AUKF) is developed and applied to nonlinear joint estimation of both time-varying states and parameters. A cost function is built based on the error...
In order to improve the performance of the UKF a novel adaptive filter method is proposed. The error between the covariance matrices of innovation measurements and their corresponding estimations/predictions is utilized as the cost function. Based on the MIT rule, an adaptive algorithm is designed to online update the covariance of the process uncertainties by minimizing the cost function. The updated...
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