An Improved Posteriori Variance-Covariance Components Estimation Applied to Unconventional GPS and Multiple Low-Cost Imus Integration Strategy
An Improved Posteriori Variance-Covariance Components Estimation Applied to Unconventional GPS and Multiple Low-Cost Imus Integration Strategy
复制标题
一种改进的后验方差-协方差分量估计应用于非常规 GPS 和多种低成本 Imus 集成策略
DOI：
10.1109/access.2019.2941996
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发表时间：
2019
期刊：
影响因子：
3.9
通讯作者：
Wang Zhenpeng
中科院分区：
文献类型：
--
作者：
Zhu Minghong;Yu Fei;Xiao Shu;Fan Shiwei;Wang Zhenpeng
卡尔曼滤波器（KF）的先验随机模型的改进一直是一个挑战。为了解决这一问题，本文采用后验方差-协方差分量估计（VCE）算法同时估计和校正所有过程噪声和测量矩阵（$\boldsymbol {Q}$ & $\boldsymbol {R}$）的方差分量，充分利用过程噪声残差、测量残差和测量冗余的贡献。不出所料，在传统的基于误差状态的集成机械化，随机模型调整是不容易的IMU，因为从惯性传感器和其他辅助传感器的可观测量之间的误差测量。该研究采用了一种非传统的多传感器集成策略，其中三维运动轨迹模型被部署为系统方程的主要部分，每个IMU的系统误差和所有传感器的测量单独建模。此外，从每个惯性传感器的测量值的权重定义的基础上的后验方差，这样我们就可以适当地分配每个测量的功能在融合算法。通过对一个包含GPS和多个伊穆斯的真实的数据集进行处理，验证了所提出的后验VCE算法的有效性。
Meliorating a priori stochastic model of Kalman filer (KF) is always challenging. To address this challenge, this paper simultaneously estimates and corrects the variance components for all of the process noise and measurement matrix ( $\boldsymbol {Q}$ & $\boldsymbol {R}$ ) by a posteriori variance-covariance components estimation (VCE) algorithm, which makes the most of the process noise residuals and measurement residuals and measurement redundancy contribution. Unsurprisingly, in the conventional error states-based integration mechanization, the stochastic model tuning is not easy for IMU because of the error measurements between the observables from inertial sensors and other aiding sensors. This research utilizes an unconventional multi-sensor integration strategy, in which a 3D kinematic trajectory model is deployed as the main part of system equation and the systematic errors of each IMU and the measurements of all sensors are individually modelled. Furthermore, the weights of measurements from each inertial sensor are defined on the basis of the posterior variances, so that we could properly distribute the function of each measurement in the fusion algorithm. A real dataset involving GPS and multiple IMUs is processed to validate the proposed posteriori VCE algorithm by applying the unconventional integration strategy.