Novel Simplex Kalman Filters

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• 作者：Wen-Jie Zhang ; Shi-Yuan Wang ; Ya-Li Feng
• 关键词：Bayesian filter ; Nonlinear Kalman filter ; State estimation ; Numerical integration ; Simplex rule
• 刊名：Circuits, Systems, and Signal Processing
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：36
• 期：2
• 页码：879-893
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Circuits and Systems; Electrical Engineering; Signal,Image and Speech Processing; Electronics and Microelectronics, Instrumentation;
• 出版者：Springer US
• ISSN：1531-5878
• 卷排序：36

文摘

In this paper, a new class of nonlinear Kalman filter is proposed to further improve filtering accuracy and efficiency for high-dimensional state estimation, which is named simplex Kalman filter (SKF). In the proposed SKF, the simplex rules of different numerical accuracy are utilized to generate two sets of sigma points that are applied to numerically compute multivariate moment integrals encountered in nonlinear Kalman filter (NKF). The proposed SKFs can achieve improvement of computational efficiency in state estimation. With the increase in the state dimension, the computational complexity of the new SKFs is lower than that of traditional NKF, which can mitigate the curse of dimension issue in high-dimensional systems. Simulations on nonlinear high-dimensional problem and bearings only tracking demonstrate that in comparison with different NKFs, the new SKFs can improve the filtering performance efficiently.