ahdi, A.E.; Azouz, A.; Abdalla, A.E.; Abosekeen, A. A Machine Learning Approach for an Improved Inertial Navigation System Solution. Sensors 2022, 22, 1687. https://doi.org/10.3390/s22041687
ahdi, A.E.; Azouz, A.; Abdalla, A.E.; Abosekeen, A. A Machine Learning Approach for an Improved Inertial Navigation System Solution. Sensors 2022, 22, 1687. https://doi.org/10.3390/s22041687
ahdi, A.E.; Azouz, A.; Abdalla, A.E.; Abosekeen, A. A Machine Learning Approach for an Improved Inertial Navigation System Solution. Sensors 2022, 22, 1687. https://doi.org/10.3390/s22041687
ahdi, A.E.; Azouz, A.; Abdalla, A.E.; Abosekeen, A. A Machine Learning Approach for an Improved Inertial Navigation System Solution. Sensors 2022, 22, 1687. https://doi.org/10.3390/s22041687
Abstract
Inertial navigation system (INS) is a basic component for obtaining a continuous navigation solution in various applications. The INS navigation solution suffers from a growing error over time. In particular, its navigation solution depends mainly on the quality and grade of the inertial measuring unit (IMU) that provides the INS with both accelerations and angular rates. However, low-cost small micro-electro-mechanical systems (MEMS) suffer from tremendous error sources such as bias, scale factor, scale factor instability, and highly non-linear noise. Therefore, MEMS-IMU measurements lead to drifted solutions when used as a control input to the INS. Accordingly, several approaches have been introduced to model and mitigate the errors associated with IMU. In this paper, a machine learning-based adaptive neuro-fuzzy inference system (ML-ANFIS) is proposed to leverage the performance of low-grade IMUs in two phases. The first phase is 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 conducted to evaluate the performance of the proposed algorithm. The results show the effectiveness of utilizing the proposed ML-ANFIS algorithm in removing errors and improving the INS solution compared to the traditional one. An improvement of 70% in the 2D-positioning and 92% in the 2D-velocity INS solution is attained when the proposed algorithm was applied compared to the traditional INS solution.
Engineering, Electrical and Electronic Engineering
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