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基于智能手机MEMS传感器的两步强大的自适应立方卡尔曼波器对行人室内定位的研究

Jijun Geng^1,2,3,4, Xuexiang Yu^1,2,3,4, Congcong Wu⁵

¹Coal Industry Engineering Research Center of Mining Area Environmental and Disaster Cooperative Monitoring, Anhui University of Science and Technology, Huainan 232001, China.

|June 28, 2023

概括

这项研究引入了一种强大的自适应卡尔曼波器,用于使用传感器融合进行智能手机室内定位. 该算法提高了行人死亡计数 (PDR) 系统的准确性和稳定性.

科学领域:

* 传感器融合和导航
* 信号处理和估计理论

背景情况:

*基于位置的服务 (LBS) 越来越依赖于室内定位.
* 带有微电机系统 (MEMS) 传感器的智能手机在室内导航中很普遍.
*行人死亡计算 (PDR) 是室内定位的一个关键技术.

研究的目的:

*为室内定位提出一个强大的适应性立方体卡尔曼波器 (RACKF) 算法.
* 为了提高基于智能手机的PDR的准确性和稳定性.
* 为了应对标题估计和步骤长度计算方面的挑战.

主要方法:

* 开发一个基于四次数的坚固适应性cubature卡尔曼波器,用于标题估计.
* 模型噪声参数的自适应性校正,使用色记忆和有限记忆权重方法.
* 基于部分状态不一致性的适应因子的构建,以减轻过模型偏差.
* 整合基于最大概率估计的可靠因子来处理测量异常值.
*使用加速度计数据和经验模型进行步骤长度估计的非线性模型构建.

主要成果:

* 拟议的两步RACKF算法显著提高了PDR准确性.
*使用三个不同的智能手机进行实验验证,证明了有效性.
* 在室内定位方面实现了1.3-1.7米的根平均平方误差 (RMSE).

结论:

* RACKF算法提高了室内PDR的适应性和稳定性.
*智能手机传感器与先进的过技术的融合提高了定位精度.
* 该方法为可靠的室内导航提供了一个实用的解决方案.

关键词:

这是一个MEMS传感器.适应因子是适应因子.室内定位装置室内定位装置强大的因素强大的因素两个阶段的 RACKFF.

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