基于强化学习的惯性导航补偿

2022 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL) Pub Date : 2022-05-08 DOI:10.1109/INERTIAL53425.2022.9787527

Eric Bozeman, Minhdao H. Nguyen, Mohammad Alam, J. Onners

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引用次数: 0

摘要

本文提出了一种应用强化学习(RL)技术在没有全球导航卫星系统(GNSS)辅助的情况下延长惯性系统停留时间的方法。使用该方法对几种强化学习算法进行了评估。在位置误差方面，对每种算法的性能结果进行了相互比较，并与独立卡尔曼滤波和导航级惯性导航系统的结果进行了比较。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Inertial Navigation Compensation with Reinforcement Learning

This paper presents a method for applying Reinforcement Learning (RL) techniques to extend the holdover time of an inertial system in the absence of aiding from a Global Navigation Satellite System (GNSS). Several RL algorithms were evaluated using this method. The performance results, in terms of positional error, for each algorithm are compared to each other as well as to the results from an unaided Kalman Filter and a navigation-grade Inertial Navigation System.

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2022 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL)

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