Kullback-Leibler divergence approach to partitioned update Kalman filter

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• 作者：Matti Raitoharju^a ; ^{matti.raitoharju@tut.fi" class="auth_mail" title="E-mail the corresponding author} ; Á ; ngel F. Garcí ; a-Ferná ; ndez^b ; Robert Piché ; ^a
• 关键词：Bayesian estimation ; Nonlinear ; Estimation ; Kalman filters ; Kullback&ndash ; Leibler divergence
• 刊名：Signal Processing
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：130
• 期：Complete
• 页码：289-298
• 全文大小：1730 K

文摘

Kalman filtering is a widely used framework for Bayesian estimation. The partitioned update Kalman filter applies a Kalman filter update in parts so that the most linear parts of measurements are applied first. In this paper, we generalize partitioned update Kalman filter, which requires the use of the second order extended Kalman filter, so that it can be used with any Kalman filter extension such as the unscented Kalman filter. To do so, we use a Kullback–Leibler divergence approach to measure the nonlinearity of the measurement, which is theoretically more sound than the nonlinearity measure used in the original partitioned update Kalman filter. Results show that the use of the proposed partitioned update filter improves the estimation accuracy.