Abstract：
The inertial navigation system (INS) is a basic component to obtain a continuous navigation solution in various applications. The INS suffers from a growing error over time. In particular, its navigation solution depends mainly on the quality and grade of the inertial measurement unit (IMU), which provides the INS with both accelerations and angular rates. However, low-cost small micro-electro-mechanical systems (MEMSs) suffer from huge error sources such as bias, the scale factor, scale factor instability, and highly non-linear noise. Therefore, MEMS-IMU measurements lead to drifts in the solutions when used as a control input to the INS. Accordingly, several approaches have been introduced to model and mitigate the errors associated with the IMU. In this paper, a machine-learning-based adaptive neuro-fuzzy inference system (ML-based-ANFIS) is proposed to leverage the performance of low-grade IMUs in two phases. The first phase was training 50% of the low-grade IMU measurements with a high-end IMU to generate a suitable error model. The second phase involved testing the developed model on the remaining low-grade IMU measurements. A real road trajectory was used to evaluate the performance of the proposed algorithm. The results showed the effectiveness of utilizing the proposed ML-ANFIS algorithm to remove the errors and improve the INS solution compared to the traditional one. An improvement of 70% in the 2D positioning and of 92% in the 2D velocity of the INS solution were attained when the proposed algorithm was applied compared to the traditional INS solution.
摘要：
惯性导航系统（INS）是在各种应用中获得连续导航解决方案的基本组件。随着时间的推移，INS遭受越来越多的错误。特别是,其导航解决方案主要取决于惯性测量单元（IMU）的质量和等级，它为INS提供加速度和角速度。然而,低成本的小型微机电系统（MEMS）遭受巨大的误差源，如偏差，比例因子，比例因子不稳定，和高度非线性噪声。因此,当用作INS的控制输入时,MEMS-IMU测量导致溶液中的漂移。因此，已经引入了几种方法来建模和减轻与IMU相关的错误。在本文中,提出了一种基于机器学习的自适应神经模糊推理系统（基于ML的ANFIS），以在两个阶段中利用低等级IMU的性能。第一阶段是用高端IMU训练50%的低等级IMU测量以生成合适的误差模型。第二阶段涉及在剩余的低等级IMU测量上测试开发的模型。使用实际道路轨迹来评估所提出算法的性能。结果表明，与传统算法相比，利用所提出的ML-ANFIS算法可以有效地消除错误并改善INS解决方案。与传统的INS解决方案相比，应用该算法后，INS解决方案的2D定位提高了70％，2D速度提高了92％。
