基于有效AP选择和多分类LDA的室内定位算法

doi:10.11959/j.issn.1000-436x.2021211

通信学报 ›› 2021, Vol. 42 ›› Issue (11): 109-120.doi: 10.11959/j.issn.1000-436x.2021211

基于有效AP选择和多分类LDA的室内定位算法

刘桂岐, 钱志鸿, 李华亮, 孙佳妮, 冯一诺, 王雪

吉林大学通信工程学院，吉林长春 130012

修回日期:2021-11-03 出版日期:2021-11-25 发布日期:2021-11-01
作者简介:刘桂岐（1986− ），女，吉林公主岭人，吉林大学博士生，主要研究方向为无线网络定位技术
钱志鸿（1957− ），男，吉林长春人，博士，吉林大学教授、博士生导师，主要研究方向为物联网、D2D、Wi-Fi、RFID等无线网络与通信技术
李华亮（1990− ），男，吉林长春人，吉林大学博士生，主要研究方向为无线定位
孙佳妮（1996− ），女，吉林长春人，吉林大学硕士生，主要研究方向为异构无线网络垂直切换技术
冯一诺（1995− ），女，吉林四平人，吉林大学硕士生，主要研究方向为异构无线网络
王雪（1984− ），女，吉林白山人，博士，吉林大学副教授，主要研究方向为物联网与D2D通信技术
基金资助:
国家自然科学基金资助项目(61771219);吉林省科技发展计划基金资助项目(20190303137SF);吉林省科技发展计划基金资助项目(20200401084GX);吉林省科技发展计划基金资助项目(20190201187JC)

Indoor positioning algorithm based on effective AP selection and multi-class LDA

Guiqi LIU, Zhihong QIAN, Hualiang LI, Jiani SUN, Yinuo FENG, Xue WANG

College of Communication Engineering, Jilin University, Changchun 130012, China

Revised:2021-11-03 Online:2021-11-25 Published:2021-11-01
Supported by:
The National Natural Science Foundation of China(61771219);Scientific and Technological Developing Scheme of Jilin Province(20190303137SF);Scientific and Technological Developing Scheme of Jilin Province(20200401084GX);Scientific and Technological Developing Scheme of Jilin Province(20190201187JC)

摘要/Abstract

摘要：

为了解决室内定位中纵向位置信息缺失的问题，提出了基于有效AP选择和多分类LDA的室内定位算法。离线阶段，采用基于稳定性及差异性的AP选择算法，提取指纹区域的有效AP集合，并利用多分类LDA算法对有效 AP 集合在不同纵向位置的指纹数据进行训练，得到纵向位置信息识别模型。在线阶段，将与离线有效 AP子集相同的AP信息输入纵向位置信息识别模型，得到识别结果，然后利用基于指纹库分区的定位算法完成在线指纹平面定位。仿真结果表明，所提算法的纵向楼层识别准确率可达到98%。

关键词: 室内定位, 指纹, 纵向位置信息识别, AP选择, 线性判别分析

Abstract:

To solve the problems of the lack of longitudinal position information in indoor positioning, an indoor positioning algorithm based on effective AP selection and multi-class LDA was proposed.In the offline stage, the AP selection algorithm based on stability and difference was used to extract effective AP set in the fingerprint area, and the multi-class LDA algorithm was adopted to train the fingerprint data of effective AP set at different heights to obtain longitudinal position information recognition model.In the online stage, the AP information alike the offline effective AP subset was input into longitudinal position information recognition model to obtain recognition result, and then the online fingerprint plane positioning was completed by the positioning algorithm based on the fingerprint database partition.Simulation results demonstrate that the accuracy of the proposed algorithm for determining longitudinal floors can reach 98%.

Key words: indoor localization, fingerprinting, longitudinal position information recognition, access point selection, LDA

中图分类号:

TN92

刘桂岐, 钱志鸿, 李华亮, 孙佳妮, 冯一诺, 王雪. 基于有效AP选择和多分类LDA的室内定位算法[J]. 通信学报, 2021, 42(11): 109-120.

Guiqi LIU, Zhihong QIAN, Hualiang LI, Jiani SUN, Yinuo FENG, Xue WANG. Indoor positioning algorithm based on effective AP selection and multi-class LDA[J]. Journal on Communications, 2021, 42(11): 109-120.

图/表 16

图1

表1

图2

图3

图4

图5

表2

图6

图7

表3

表4

表5

图8

表6

EAP-LDA算法主要仿真参数"

参数	数值
rss_thr	${median(rss}_{{ap}_{1}} {,rss}_{{ap}_{2}}, ... {,rss}_{ap}_{_{m}})$
rss_r,th	${median(rss}_{ap}_{_{1}} {,rss}_{ap}_{_{2}}, ... {,rss}_{ap}_{_{m}})$
α	0.75
m_ap	5
m'	8

表6

表7

图9

参考文献 16

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建筑物	F	N_f	N_t	N_ap
T₀	4	5 249	536	200
T₁	4	5 196	307	207
T₂	5	9 492	268	203

楼层	T₀	T₁	T₂
第一层	99%	98%	98%
第二层	97%	97%	96%
第三层	97%	98%	96%
第四层	98%	98%	97%
第五层	—	—	98%
平均	98%	98%	97%

参考点	161	51	162	52	25
1	-100	-100	-100	-100	-100
2	-95	-100	-94	-100	-80
3	-71	-87	-71	-88	-92
4	-65	-77	-71	-77	-100
5	-85	-100	-83	-100	-91
6	-87	-100	-88	-100	-89

参考点	第一维	第二维	第三维
1	66.1492	78.4905	0.1239
2	48.1170	65.7072	-0.2531
3	50.1185	80.3818	-0.1433
4	61.7909	84.0203	3.8362
5	48.9408	79.0238	-1.0936
6	50.0295	76.5652	-0.9758

楼层	均值	方差
第一层	(61.7,79.2,-0.099)	(39.8, 43.1, 7.35)
第二层	(55.8,79.5,0.117)	(99.7,124.0, 2.16)
第三层	(65.2,76.1,0.313)	(58.0,46.2,11.73)
第四层	(74.9,71.4,0.045)	(89.5,49.0,9.12)

基于有效AP选择和多分类LDA的室内定位算法

Indoor positioning algorithm based on effective AP selection and multi-class LDA

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图/表 16

参考文献 16

相关文章 15

Metrics

推荐阅读 0

算法	T₀	T₁	T₂
k-means算法	0.84	0.83	0.78
PLGD	0.90	0.91	0.89
MA-LDA	0.86	0.85	0.81
EAP-LDA	0.98	0.98	0.97

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