基于粒子滤波联合算法的地磁室内定位

黄鹤; 仇凯悦; 李维; 罗德安

doi:10.3969/j.issn.0258-2724.20170927

基于粒子滤波联合算法的地磁室内定位

doi: 10.3969/j.issn.0258-2724.20170927

黄鹤^1,2,,
仇凯悦¹,
李维¹,
罗德安^1,2, ,

基金项目: 国家重点研发计划资助项目（2017YFB0503702）

详细信息

作者简介:
黄鹤（1977—），男，副教授，研究方向为室内导航定位、智能驾驶高精度导航地图等，E-mail：huanghe@bucea.edu.cn

通讯作者:
罗德安（1968—），男，教授，研究方向为GNSS理论及应用、三维激光数据处理理论及应用等，E-mail：luodean@bucea.edu.cn

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出版历程
- 收稿日期: 2018-01-09
- 修回日期: 2018-05-14
- 网络出版日期: 2018-05-30
- 刊出日期: 2019-06-01

Indoor Geomagnetic Positioning Based on Joint Algorithm of Particle Filter

HUANG He^{1,2
,},
QIU Kaiyue¹,
LI Wei¹,
LUO Dean^{1,2
, ,}

Funds: National Key Research and Development Program of China，No.2017YFB0503702

摘要

摘要: 室内地磁场受钢结构与其它铁磁材料的影响，造成磁场区域局部异常，使室内地磁场具有特异性. 受益于此种现象，室内地磁定位技术得以实现. 然而在大型建筑中地磁场的特异性会减弱，这导致定位结果出现模糊现象. 针对这一现象，文中提出了基于路径匹配的室内地磁定位技术，通过增加匹配特征数量来解决此问题. 使用基于动态时间规整（dynamic time warp，DTW）算法与粒子滤波（particle filter，PF）算法的新型联合算法，并以路径匹配的模式对目标进行追踪. 在匹配过程中又通过计算斯皮尔曼等级（Spearman）相关系数确定路径之间的相似度，使之作为辅助定位. 最后用装载了磁传感器的测量机器人进行实验验证，结果表明：路径匹配具有足够的地磁特征数量，能够解决特异性减弱情况下定位结果模糊现象，且定位精度优于1 m.
- 粒子滤波 /
- 地磁场 /
- 动态时间规整 /
- 室内定位系统 /
- 目标跟踪
Abstract: The indoor geomagnetic field is affected by the presence of a steel structure and other ferromagnetic materials, causing local anomalies in the magnetic field region and and a unique indoor magnetic field. Indoor geomagnetic positioning technology can be realized by exploiting this phenomenon. However, in large buildings, the specificity of the geomagnetic field is weakened, which leads to the distortion of positioning results. To overcome this problem, an indoor geomagnetic positioning technology based on path matching was proposed, which increases the number of matching features. A new joint algorithm was used combining the dynamic time warp algorithm with the particle filter algorithm, which can track the target in a path matching mode. In the matching process, the similarity between the paths was determined by calculating the Spearman correlation coefficient, to assist the positioning. Finally, experimental verification was performed using a measuring robot loaded with a magnetic sensor. The results show that the path matching exhibits a sufficient number of geomagnetic features, which can overcome the phenomenon of blurring the positioning results under the condition of weakening specificity, and the positioning accuracy is higher than 1 m.
- particle filter /
- geomagnetic field /
- dynamic time warp /
- indoor positioning systems /
- target tracking

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图 1 定位流程框架

Figure 1. Framework of the positioning process

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图 2 联合算法流程

Figure 2. Process flow of the joint algorithm

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图 3 走廊平面图以及地磁参考图

Figure 3. Corridor plan and geomagnetic reference map

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图 4 测量机器人结构

Figure 4. Structure of a measurement robot

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图 5 点位与路径长度关系

Figure 5. Relationship between points and path length

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图 6 不同粒子数随着路径增加的定位结果

注：图中的数字编号代表粒子数，即1表示N = 100粒子

Figure 6. Positioning results of different particle numbers increasing with path

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图 7 误差占比（e < 1 m）

Figure 7. Proportion of error（e < 1 m）

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图 8 同一路径基于粒子滤波器单点定位结果（N = 400）

Figure 8. Positioning results of single point for the same path based on particle filter （N = 400）

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图 9 粒子滤波器基于单点匹配和联合算法基于路径匹配的误差占比以及匹配时间

Figure 9. Error ratio and matching time of the particle filter based on single point matching and joint algorithm based on path matching

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图 10 定位结果对比（N = 400）

Figure 10. Comparison of positioning results （N = 400）

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图 11 不同路径定位误差比较

Figure 11. Comparison of positioning errors for different paths

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表 1 不同粒子数匹配时间

Table 1. Time required to match different particle counts

N 100 200 300 400 500 600

时间/s 1.92 2.96 3.84 4.73 5.72 6.71

下载: 导出CSV

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