利用渐消自适应EKF算法进行PDR-WiFi融合定位

胡安冬; 王坚; 汪云甲; 刘春燕; 谭兴龙; 李增科

doi:10.13203/j.whugis20140432

利用渐消自适应EKF算法进行PDR-WiFi融合定位

胡安冬^{1, 2, 3,},
王坚^{1, 2, ,},
汪云甲^{1, 2},
刘春燕^{1, 2},
谭兴龙^{1, 2},
李增科^{1, 2}

1.
中国矿业大学国土环境与灾害监测国家测绘局重点实验室, 江苏徐州, 221116
2.
中国矿业大学环境与测绘学院, 江苏徐州, 221116
3.
墨尔本皇家理工大学数学与地理信息系, 澳大利亚

基金项目:

国土资源部公益性行业科研专项 201411007-1

详细信息

作者简介:
胡安冬, 硕士生, 主要从事室内定位及数据处理方面的研究。han_winter@foxmail.com

通讯作者:
王坚, 博士, 教授。wjiancumt@163.com

中图分类号: P228
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出版历程
- 收稿日期: 2015-11-03
- 发布日期: 2016-11-04

An Fusion Positioning for PDR and WiFi Based on Fading Adaptive Weighted EKF

HU Andong^{1, 2, 3,},
WANG Jian^{1, 2, ,},
WANG Yunjia^{1, 2},
LIU Chunyan^{1, 2},
TAN Xinglong^{1, 2},
LI Zengke^{1, 2}

1.
Key Laboratory of Land Environment and Disaster Monitoring, National Administration of Surveying, Mapping and Geoinformation, China University of Mining and Technology, Xuzhou 221116, China
2.
School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
3.
School of Mathematical and Geospatial Sciences, RMIT University, Melbourne, VIC, Austrlia

Funds:

The Ministry of Land and Resources Public Welfare Industry Special Scientific Research 201411007-1

More Information

Author Bio:
HU Andong, postgraduate, specializes in the indoor positioning and data processing. E-mail:han_winter@foxmail.com

Corresponding author:
WANG Jian, PhD, professor. E-mail:wjiancumt@163.com

摘要

摘要: 针对基于指纹库的WiFi定位存在的点位重积、回跳，行人航位推算算法中误差积累的问题，提出了并实现了通过一种自适应加权扩展卡尔曼滤波对两种定位算法进行松耦合。首先给出了WiFi无线定位和行人航位推算进行位置解算的原理，采用渐消因子的自适应加权EKF算法实现了两者的融合，最后通过实测数据验证算法的有效性。试验表明，该方法在保持了WiFi定位单次定位高精度的特性的同时，继承了航位推算的连贯性，不仅减少了WiFi定位所存在的重复堆积点以及回跳点，并在一定程度上削弱了行人航位推算所存在的积累误差，提高了融合算法的效率，大大提高了室内定位的精度与稳定性。
- 室内定位 /
- 行人航位推算 /
- 扩展卡尔曼滤波 /
- WiFi /
- 渐消因子 /
- 自适应加权
Abstract: According to the accumulation of points within WiFi locations based on the fingerprint map database, and error accumulation calculated by Pedestrian Dead Reckoning, a loose fusion coupling algorithm by Adaptive Weighted Extended Kalman Filter is presented. This method maintains the high-precision of WiFi locations. In the meanwhile, the algorithm inherited the coherence from PDR(Pedestrian Dead Reckoning), which not only decreased the accumulated rebound points, but weakened the error accumulation, enhanced the efficiency of the fusion algorithm, and finally improved the precision and stability of indoor localization. The result denotes that this method works in the indoor environment quite well, which improves almost 22.9% according to WiFi results.
- indoor localization /
- pedestrian dead reckoning /
- extended Kalman filter /
- WiFi /
- fading factor /
- adaptive weighted

HTML全文

图 1 各因子误差结果总图

Figure 1. Error of Total Results

下载: 全尺寸图片幻灯片

图 2 渐消因子数值图

Figure 2. Value of Fading Factor

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图 3 试验场区域图

Figure 3. The Test Area

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图 4 实验结果

Figure 4. Results of Experiment

下载: 全尺寸图片幻灯片

图 5 4种方法误差图

Figure 5. Error of Four Methods

下载: 全尺寸图片幻灯片

表 1 自适应因子及其中误差

Table 1 Adaptive Factor with Its Error

	数值
自适应因子	1	1.1	1.2	1.3	1.4	1.5
中误差/m	3.110	3.024	2.999	3.017	3.302	3.063

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表 2 4个方案各自的误差分析

Table 2 Error Analysis of Different Location Algorithms

	方案1	方案2	方案3	方案4
中误差/m	4.037	5.993	3.111	2.618
平均误差/m	3.135	4.990	2.904	2.344
最大误差/m	10.375	12.650	4.829	4.708

下载: 导出CSV

表 3 辅助粒子滤波不同抽样数的中误差与运算时间

Table 3 Error and Computing Time for Particle Filter with Different Samples

单点粒子数	100	200	300	400	500
中误差/m	4.34	3.58	3.25	3.09	3.12
运算时间/s	1.218	2.867	6.536	10.798	16.048

下载: 导出CSV

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