惯性导航系统
卡尔曼滤波器
计算机科学
全球导航卫星系统应用
离群值
控制理论(社会学)
导航系统
滤波器(信号处理)
状态变量
扩展卡尔曼滤波器
噪音(视频)
全球定位系统
算法
数学
方向(向量空间)
人工智能
计算机视觉
电信
物理
几何学
控制(管理)
图像(数学)
热力学
作者
Hongpo Fu,Yongmei Chen
摘要
Summary This article investigates the state estimation problem of the nonlinear system with the large process prior uncertainty but high‐accuracy measurement information, the situation is prone to occur in the inertial navigation system (INS)/global navigation satellite system (GNSS) tightly coupled navigation system. Furthermore, the unknown heavy‐tailed measurement noises induced by the inaccurate prior navigation information and random measurement outliers are also considered. Given existing methods such as progressive cubature Kalman filter (PCKF) cannot effectively solve the above issues, a novel robust PCKF with variable step size (RVSS‐PCKF) is proposed. First, a new Gaussian‐uniform‐mixing inverse Gamma (GUMIG) distribution is presented to model the variable step size and measurement noise. Next, the GUMIG distribution is expressed as a hierarchical Gaussian presentation and then RVSS‐PCKF is derived with the help of the variational Bayesian (VB) inference. In the filter, the state, variable step size and noise statistic are jointly estimated by the fixed‐point iterations. Finally, the simulations and real data of the tightly coupled navigation illustrate the superiority of the filter in terms of accuracy and steady‐state performance.
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