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Robust H-infinity CKF/KF hybrid filtering method for SINS alignment

机译：用于SINS对准的鲁棒H无限CKF / KF混合滤波方法

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摘要

This study concerns the in-motion alignment in the strapdown inertial navigation system (SINS) with large misalignment angles. As the non-linear filtering method applied in the alignment model is quite computer intensive, which has a significant impact on the alignment accuracy and speed. To solve this problem, a robust H-infinity cubature Kalman filter (CKF)/KF hybrid filter (RHCHF) is proposed to lower the computational burden and strengthen the robustness. By virtue of the idea of model decomposition, the RHCHF could estimate the non-linear and linear parts of alignment model, respectively. Through the introduction of robust factor to adjust the filter parameters, it can ensure the accuracy reliably. The comparisons of the simulation and vehicle experiment demonstrate that the RHCHF could achieve the results at a significantly lower expense than the unscented Kalman filter, and obtain a high accuracy even when the statistical property of noise is uncertain or the outliers of measurement occur occasionally.
机译：这项研究涉及具有大的未对准角度的捷联惯性导航系统（SINS）中的运动对准。由于在对准模型中应用的非线性滤波方法需要大量的计算机资源，因此对对准精度和速度产生了重大影响。为了解决这个问题，提出了一种鲁棒的H-无限库尔曼卡尔曼滤波器（CKF）/ KF混合滤波器（RHCHF），以减轻计算负担，增强鲁棒性。借助模型分解的思想，RHCHF可以分别估计对准模型的非线性和线性部分。通过引入鲁棒因子来调整滤波器参数，可以可靠地确保精度。仿真和车辆实验的比较表明，RHCHF可以以比无味卡尔曼滤波器低得多的成本获得结果，并且即使在噪声的统计特性不确定或偶尔出现测量异常时也可以获得很高的精度。

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