多传感器导航系统中神经模糊模块更新程序的改进

A. Noureldin
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引用次数: 2

摘要

陆地车辆主要依靠全球定位系统(GPS)来提供一致精度的位置。然而,在卫星信号被阻断的城市地区,GPS接收器可能会遇到频繁的GPS中断。为了克服这个问题,GPS通常与安装在车辆内部的惯性传感器(加速度计和陀螺仪)相结合,以获得可靠的导航解决方案,特别是在GPS中断期间。神经模糊模块用于实时融合GPS和惯性传感器数据。本研究通过在更新过程中实施基于时间窗口的交叉验证方法来优化这些模块的操作。现场试验数据用于检验所提出的方法的性能,结果讨论了所提出的技术的优点和局限性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing the Update Procedure of Neuro - Fuzzy Modules Used for Multi-Sensor Navigation Systems
Land vehicles rely mainly on Global Positioning System (GPS) to provide its position with consistent accuracy. However, GPS receivers may encounter frequent GPS outages within urban areas where satellite signals are blocked. In order to overcome this problem, GPS is usually combined with inertial sensors (accelerometers and gyroscopes) mounted inside the vehicle to obtain a reliable navigation solution, especially during GPS outages. Neuro-fuzzy modules were used to fuse data from GPS and inertial sensors in real-time. This research optimizes the operation of these modules by implementing a temporal window-based cross-validation approach during the update procedure. A field test data was used to examine the performance of the proposed method and the results discuss the merits and the limitations of the proposed technique.
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