高斯分布
非线性系统
滤波器(信号处理)
估计员
计算机科学
算法
高斯滤波器
高保真
自适应滤波器
国家(计算机科学)
高斯过程
卡尔曼滤波器
数学优化
控制理论(社会学)
数学
人工智能
统计
工程类
控制(管理)
物理
电气工程
图像(数学)
量子力学
计算机视觉
作者
Bin Zhang,Yung C. Shin
标识
DOI:10.1016/j.sigpro.2022.108677
摘要
The state estimation of highly nonlinear dynamic systems is difficult because the probability distribution of their states can be highly non-Gaussian. An adaptive Gaussian mixture filter is developed in this work to address this challenge, in which the Gaussian mixture models are refined based on the system's local severity of nonlinearity to attain a high-fidelity estimation of the state distribution. A set of nonlinearity assessment criteria are designed to trigger the splitting of Gaussian components at both the prediction and update stages of Bayesian filtering and the error bound of estimated distribution is established. The new filter has been benchmarked against the existing methods on two challenging problems and it consistently provides among-the-best accuracy with a reasonable computational cost, which proves that it can be used as a reliable state estimator for engineering systems with highly nonlinear dynamics and subject to high magnitudes of uncertainties.
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