适应性多阶段混合定位用于RIS辅助的6G室内定位系统:将指纹和几何方法与条件感知融合方法相结合
Iacovos Ioannou1,2,3, Vasos Vassiliou2,3, Marios Raspopoulos4,5
1Department of Computer Science and Engineering, European University Cyprus, 1516 Nicosia, Cyprus.
Sensors (Basel, Switzerland)
|February 27, 2026
概括
本研究介绍了一种新的自适应本地化算法,使用多个可重新配置的智能表面 (RIS) 进行精确的室内定位. 与现有技术相比,新方法显著提高了准确性,并减少了错误.
科学领域:
- 无线通信无线通信
- 信号处理 信号处理
- 在本地化技术技术.
背景情况:
- 可重新配置的智能表面 (RIS) 正在成为6G无线网络的关键技术,可以精确控制电磁波传播.
- 高精度的室内定位对于各种应用至关重要,包括机器人,增强现实和资产跟踪.
研究的目的:
- 开发一个高精度的室内定位框架,使用合作的多RIS部署.
- 引入自适应式多阶段混合本地化 (AMSHL) 算法,结合指纹和几何时间差距到达 (TDoA) 方法.
- 通过自适应融合和先进的信号处理技术来提高定位的准确性和稳定性.
主要方法:
- 实施一个4-RIS合作架构,以实现全面的空间覆盖.
- 开发AMSHL算法,其中包括混合指纹数据库 (RSSI和TDoA),多阶段级联精细化和自适应融合.
- 集成一个强大的代重量最小方程 (IRLS) 解析器与胡贝尔M估计和贝叶斯规范化.
- 使用蒙特卡洛模拟,使用3.5GHz载波频率,带宽为400MHz.
主要成果:
- AMSHL算法实现了0.661m的中位定位误差和1.54m的根平均平方误差 (RMSE).
- 与传统方法相比,显示了87.5%的-2m以下准确度的概率,这代表了中位误差的4.9倍改善和平均平方误差 (MSE) 的7.1倍减少.
- 可选的基于西格体的融合变体 (AMSHL-S) 进一步提高了2m以下的精度,达到89.4%.
结论:
- 拟议的AMSHL框架在使用RIS技术的高精度室内定位方面取得了重大进展.
- 适应融合机制和强大的估计技术有助于卓越的准确性和可靠性.
- 公开可用的模拟框架促进了RIS辅助定位系统的可重复性研究.
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