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基于NLOS角度歧视和MAP约束的UWB/MEMS IMU综合定位方法

Xin Sui¹, Jiapeng Song², Changqiang Wang¹

¹School of Geomatics, Liaoning Technical University, Fuxin, 123000, China.

Scientific reports

|August 27, 2024

概括

本研究介绍了超宽带 (UWB) 信号的动态非视线 (NLOS) 角度歧视方法. 这种新的算法提高了UWB/MEMS IMU在动态,封闭的环境中的定位准确性.

科学领域:

导航和定位系统导航和定位系统
信号处理信号处理
机器人技术和自主系统

背景情况:

超宽带 (UWB) 信号在动态环境中面临局部化挑战,原因是非视线 (NLOS) 条件和信号封闭.
现有的NLOS对UWB信号在动态设置中的错误表征不足,阻碍了可靠的定位.
信号封闭显著影响UWB的范围精度,特别是在特定的水平角度.

研究的目的:

为UWB信号提出一个动态的NLOS角度区分方法,以提高定位精度.
开发一个紧密集成的UWB/MEMS IMU定位算法,以计算动态NLOS特征.
提高UWB定位在信号阻塞和多路径效应的环境中的稳定性.

主要方法:

基于水平角度的动态NLOS角度区分技术被开发出来,以识别和权衡UWB范围的观测.
实现了一个结合UWB和MEMS IMU数据的紧密集成定位算法,结合NLOS角度歧视和地图约束.
分析了UWB信号封闭特征,涉及水平角度,以了解范围错误行为.

主要成果:

拟议的算法实现了较低的定位误差:0.189m (向北),0.126m (向东) 和0.243m (平面) 在最初的实验中.
进一步的实验产生了改进的误差:0.119m (向北),0.134m (向东),和0.211m (平面).
与强大的自适应卡尔曼过算法相比,飞机的位置精度显示了22.9%和28.5%的显著改进.

结论:

动态NLOS角度区分方法有效地减轻了封闭环境中的UWB距离错误.
与传统方法相比,集成的UWB/MEMS IMU算法展示了优越的定位性能,特别是在具有挑战性的动态场景中.
这些发现有助于在现实应用中使用UWB技术的更可靠和更准确的本地化系统.

关键词:

动态的NLOS角度歧视.快速线段匹配的快速线段匹配地图数据库地图数据库强大的卡尔曼过器在UWB/MEMS IMU 集成中,UWB/MEMS 集成是 IMU 集成.

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