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长期短期记忆神经网络算法与卡尔曼波器融合在UWB室内定位中的应用

Yalin Tian¹, Zengzeng Lian², Penghui Wang¹

¹School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, 454003, China.

Scientific reports

|January 22, 2024

概括

这项研究引入了卡尔曼波器-长短期记忆 (KF-LSTM) 算法,通过减少随机错误来提高超宽带 (UWB) 室内定位精度. KF-LSTM方法显著提高了定位精度和稳定性,特别是在杂的环境中.

科学领域:

工程工程师工程师工程师
计算机科学计算机科学
信号处理信号处理

背景情况:

超宽带 (UWB) 技术为室内定位提供强大的防干扰.
UWB系统容易发生随机错误,影响定位准确性.
卡尔曼波器 (KF) 和长短期存储器 (LSTM) 网络在降噪和时间序列分析方面分别有效.

研究的目的:

提出和评估一种新的KF-LSTM算法,以提高UWB室内定位精度.
利用KF的噪声过能力和LSTM的序列建模优势.
证明拟议的算法的优越性能与现有方法相比.

主要方法:

使用卡尔曼波器 (KF) 预处理UWB数据以减轻噪声.
用KF过的数据训练长期短期记忆 (LSTM) 网络,以准确地估计位置.
对KF-LSTM算法与反向传播 (BP) 网络,KF-BP和独立的LSTM算法的比较分析.

主要成果:

在平均定位精度方面,KF-LSTM算法取得了显著的改进:比BP高出71.31%,比KF-BP高出37.28%,比LSTM高出49.31%.
与其他算法相比,KF-LSTM算法表现出更优越,更稳定的性能.
性能优势,特别是稳定性,随着UWB数据中的噪声水平的增加而变得更加明显.

结论:

拟议的KF-LSTM算法有效地提高了UWB室内定位的准确性和稳定性.
结合KF和LSTM,为减轻噪声和改进时间序列定位数据提供了强大的解决方案.
这种混合方法为先进的UWB定位系统提供了有希望的方向,特别是在具有挑战性的,杂的环境中.

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