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arXiv · 1503.08379

Consider Uncertain Parameters based on Sensitivity Matrix

Taishan Lou
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Abstract

Uncertain parameters of state-space models have always been a considerable problem. Consider Kalman filter (CKF) and desensitized Kalman filter (DKF) are two methods to solve this problem. Based on the sensitivity matrix respected to the uncertain parameter vector, a special DKF with an analytical gain is given and a new form of the CKF is derived. The mathematical equivalence between the special DKF and the CKF is demonstrated when the sensitivity-weighting matrix is set to the covariance of the uncertain parameter and the problem how to select and obtain the sensitivity-weighting matrix in the DKF is solved.

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Taishan Lou. 2015-03-29. Consider Uncertain Parameters based on Sensitivity Matrix. https://arxiv.org/abs/1503.08379

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