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对GNSS/IMU系统进行对象定位和空间位置估计的研究

Rosen Miletiev¹, Peter Z Petkov¹, Rumen Yordanov²

¹Faculty of Telecommunication, Technical University of Sofia, 1000 Sofia, Bulgaria.

Sensors (Basel, Switzerland)

|November 27, 2025

概括

这项研究集成了全球导航卫星系统 (GNSS) 和惯性测量单元 (IMU) 传感器,以实现精确的导航. 使用卡尔曼波器的传感器融合提高了自主系统的准确性,改善了位置和方向跟踪.

科学领域:

机器人技术和自主系统
地理学工程工程地质学
传感器融合式传感器

背景情况:

导航系统对于自动驾驶汽车,VR/AR和对象跟踪至关重要.
准确的3D位置和定向跟踪对于这些应用是必不可少的.
惯性导航系统 (INS) 提供运动数据,但需要与其他传感器融合以实现精确的导航.

研究的目的:

将全球导航卫星系统 (GNSS) 与10度自由度 (10DoF) 惯性测量单位 (IMU) 系统集成.
准确计算物体的位置,态度和方向.
评估传感器数据融合技术,以提高导航性能.

主要方法:

传感器数据融合使用两个卡尔曼波器 (KF) 进行位置和态度计算.
对三台MEMS IMU传感器的艾伦方差和正常分布参数进行了详细分析.
使用商业和拟议天线的GNSS系统的性能评估.
实验验证比较KF方向角度输出与其他来源的实验验证.

主要成果:

拟议的传感器融合方法提高了导航任务的准确性.
艾伦差异和分布分析为MEMS IMU传感器特征提供了洞察力.
评估GNSS天线的性能,以实现最佳的系统集成.
实验结果验证了基于卡尔曼波器的融合对标题估计的有效性.

结论:

将GNSS和IMU系统与基于卡尔曼波器的传感器融合集成,为3D导航提供了一个强大的解决方案.
了解传感器特性对于优化过器性能至关重要.
该研究表明,自主应用程序的位置,态度和方向计算的准确性得到了改进.

关键词:

艾伦·艾伦变量变量在GNSS中使用GNSS.在IMU,IMU是IMU.卡尔曼过器可以过.在四季节期间,四季节是四季节.

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