基于INS/UWB紧组合的行人室内定位方法

doi:10.12305/j.issn.1001-506X.2025.04.26

摘要/Abstract

摘要：

针对室内复杂应用场景下行人定位精度低的问题, 提出一种基于惯性导航系统/超宽带(inertial navigation system/ultra-wide band, INS/UWB)紧组合定位方法。以脚部运动的一个步态周期为基本搜索单元, 通过行人脚部运动分析, 利用z轴角速度信息实现行人复合运动下的精确零速检测, 进一步利用零速校正(zero velocity update, ZUPT)抑制惯性解算累积误差。同时, 考虑UWB测距精度受非视距(non-line-of-sight, NLOS)影响问题, 利用反向传播神经网络自适应学习方法建立NLOS误差模型并据此对UWB测距误差进行补偿。实验结果表明, 在室内复杂应用场景下，所提INS/UWB紧组合算法定位误差为0.19 m, 相比未进行ZUPT和NLOS误差补偿的INS/UWB紧组合定位方法，定位精度提高84.6%。

关键词: 行人室内定位, 超宽带, 紧组合, 零速校正, 非视距误差

Abstract:

In view of the problem of poor pedestrian positioning accuracy in the indoor complex application scene, the inertial navigation system/ultra-wide band (INS/UWB) tight integrated positioning algorithm is proposed. Taking one gait cycle of foot motion as a basic search unit and by analyzing pedestrian's foot motion, z-axis angular velocity information is used to detect zero-speed state under composite motions of pedestrians accurately, so as to further suppress inertial solution accumulative error with zero velocity update (ZUPT). Meanwhile, considering the impact of non-line-of-sight (NLOS) on UWB ranging accuracy, an NLOS error model is established using a backpropagation neural network adaptive learning method to compensate for UWB ranging errors. The experimental results show that the INS/UWB tight integration algorithm proposed in indoor complex application scene has a positioning error of 0.19 m, which is 84.6% higher than the INS/UWB tight integration positioning method without ZUPT and NLOS error compensation.

Key words: pedestrian indoor positioning, ultra-wide band (UWB), tight integration, zero velocity update (ZUPT), non-line-of-sight (NLOS) error

中图分类号:

TH89

奔粤阳, 黄原, 黄鸿殿, 李倩. 基于INS/UWB紧组合的行人室内定位方法[J]. 系统工程与电子技术, 2025, 47(4): 1300-1310.

Yueyang BEN, Yuan HUANG, Hongdian HUANG, Qian LI. Pedestrian indoor positioning method based on INS/UWB tight integration[J]. Systems Engineering and Electronics, 2025, 47(4): 1300-1310.

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检测器	零速数量/帧	终点误差/m
无	0	23.970
三阈值法	438	7.863
零速检测法	589	9.642
自适应零速区间搜索算法	657	4.851

检测器	零速数量/帧	终点误差/m
无	0	48 575
三阈值法	9 936	270.412
零速检测法	6 892	192.108
自适应零速区间搜索算法	14 860	100.606

检测器	相同零速数量/帧	匹配度/%
三阈值法	400	91.324
零速检测法	559	94.907

检测器	相同零速数量/帧	匹配度/%
三阈值法	9 936	90.58
零速检测法	6 892	94.31

样本	点位测量数据			BPNN预测
样本	真实距离/m	测量距离/m	更正前绝对误差/m	更正后测量距离/m	更正后绝对误差/m
A1	4.30	4.53	0.23	4.29	0.01
A2	5.30	5.52	0.22	5.28	0.02
A3	4.89	5.21	0.32	4.90	0.01
A4	3.50	3.68	0.18	3.53	0.03
A5	2.03	2.39	0.36	2.01	0.02
A6	2.11	2.50	0.39	2.12	0.01
A7	1.92	2.10	0.18	1.94	0.02
A8	4.05	4.28	0.23	4.08	0.03
A9	3.46	3.70	0.26	3.47	0.01
A10	2.59	2.86	0.27	2.62	0.03