A Hybrid EKF/WUFIR Filter for Indoor Localization Integrating INS and UWB Data

IF 6.7 2区计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY

IEEE Transactions on Network Science and Engineering Pub Date : 2025-03-03 DOI:10.1109/TNSE.2025.3546918

Long Cheng;Jiahe Song;Wenhao Zhao

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引用次数: 0

Abstract

Due to the complex and variable indoor environment, ultra-wideband (UWB) signal transmission is often obstructed by walls and obstacles, resulting in non-line-of-sight (NLOS), which reduces localization accuracy. Inertial navigation system (INS) is an autonomous navigation system that does not rely on external information and is not affected by NLOS. Therefore, a hybrid EKF/WUFIR filter indoor localization algorithm that integrates INS and UWB data is proposed. The proposed algorithm is composed of three parts: INS localization, UWB localization and data fusion. In the INS localization part, the motion model is used to determine the state of the target in real time using measurement data obtained from the inertial measurement unit (IMU). In the UWB localization part, a resettable residual weighted particle filter algorithm is proposed to mitigate the effect of NLOS on the localization results. In the data fusion part, a hybrid filtering algorithm combining extended Kalman filter (EKF) and weighted unbiased finite impulse response (WUFIR) filtering is proposed to fuse the INS and UWB localization data. Simulation and experimental results show that the proposed algorithm outperforms other comparative algorithms in terms of robustness and localization accuracy.

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结合INS和UWB数据的室内定位混合EKF/WUFIR滤波器

由于室内环境复杂多变，超宽带（UWB）信号传输经常受到墙壁和障碍物的阻碍，产生非视距（NLOS），降低了定位精度。惯性导航系统（INS）是一种不依赖外部信息、不受NLOS影响的自主导航系统。为此，提出了一种结合惯导系统和超宽带数据的混合EKF/WUFIR滤波器室内定位算法。该算法由惯性定位、超宽带定位和数据融合三部分组成。在INS定位部分，利用惯性测量单元（IMU）获得的测量数据，利用运动模型实时确定目标的状态。在超宽带定位部分，提出了一种可重置残差加权粒子滤波算法，以减轻NLOS对定位结果的影响。在数据融合部分，提出了扩展卡尔曼滤波（EKF）和加权无偏有限脉冲响应滤波（WUFIR）相结合的混合滤波算法，用于融合惯导系统和超宽带定位数据。仿真和实验结果表明，该算法在鲁棒性和定位精度方面都优于其他比较算法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Network Science and Engineering Engineering-Control and Systems Engineering

CiteScore

12.60

自引率

9.10%

发文量

393

期刊介绍： The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.