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《山东大学学报(理学版)》 ›› 2019, Vol. 54 ›› Issue (5): 8-20.doi: 10.6040/j.issn.1671-9352.2.2018.200

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基于WiFi定位的区域人群轨迹模型

徐洋1,2(),孙建忠1,黄磊2,谢晓尧1   

  1. 1. 贵州师范大学贵州省信息与计算科学重点实验室，贵州 贵阳 550001
    2. 贵州师范大学—贵阳市公安局信息安全联合研究中心，贵州 贵阳 550001
  • 收稿日期:2018-07-19 出版日期:2019-05-20 发布日期:2019-05-09
  • 作者简介:徐洋(1983—),男,博士(后),教授,硕士生导师,主要研究方向为网络安全、信息系统安全. E-mail:xy@gznu.edu.cn
  • 基金资助:
    国家自然科学基金重点项目(61332019);中央引导地方科技发展专项资金项目(黔科中引地20184008);贵州省科技合作计划重点项目(黔科合LH字20157763);住房和城乡建设部科学技术计划项目(2016-K3-009);全国统计科学研究项目(2016LY81)

Trajectory model of area crowd based on WiFi positioning

Yang XU1,2(),Jian-zhong SUN1,Lei HUANG2,Xiao-yao XIE1   

  1. 1. Key Laboratory of Information and Computing Science of Guizhou Province, Guizhou Normal University, Guiyang 550001, Guizhou, China
    2. Guizhou Normal University-Guiyang Public Security Bureau Joint Research Centre for Information Security, Guiyang 550001, Guizhou, China
  • Received:2018-07-19 Online:2019-05-20 Published:2019-05-09
  • Supported by:
    国家自然科学基金重点项目(61332019);中央引导地方科技发展专项资金项目(黔科中引地20184008);贵州省科技合作计划重点项目(黔科合LH字20157763);住房和城乡建设部科学技术计划项目(2016-K3-009);全国统计科学研究项目(2016LY81)

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摘要：

针对传统的室内WiFi定位方法难以解决大型活动及区域间流动人群轨迹分析需要这一问题，提出了基于三边测量定位和信号强度(RSSI)的应用于大型场馆、复杂环境下的人群定位新方法，实现区域内人员定位、区域内外人群划分、区域内人群流量分析。使用基于一种概率统计预测算法进行人群轨迹预测，建立了WiFi区域内人群轨迹模型，通过进一步建立的跨区域人群移动轨迹模型，实现大跨度区域间人群流动分析。通过搭建WiFi区域人群轨迹模型验证系统，使用2016年贵阳数博会数据，进行了数据可视化分析，证明了模型的有效性。

关键词: WiFi定位, 人群轨迹, 三边测量定位, 信号强度, 位置指纹

Abstract:

The existing indoor WiFi positioning methods are difficult to resolve the need of crowd trajectory analysis in the large-scale activities and inter-regional. Aiming at this problem, a new crowd location method based on trilateration measurement and signal strength indication (RSSI) for large-scale venues and complex environments is proposed. Crowd positioning in the region, crowd division inside and outside the region, and crowd flow analysis in the region can be achieved. A prediction algorithm based on probability and statistics is used to predict crowd trajectories. A crowd trajectory model in WiFi area is established. A cross regional crowd mobility trajectory model is further established to analyse the flow of crowd in large area inter regional. A proving system of WiFi regional crowd trajectory model is established. Using the data of 2016 Guiyang International Big Data Expo, data visualization analysis is carried out. In this way, the validity of the model is proved.

Key words: WiFi positioning, crowd trajectory, trilateration, received signal strength indication, location fingerprint

中图分类号: 

  • TP391

图1

三边测量定位示意图"

图2

位置指纹定位原理"

图3

定位示意图"

图4

场馆示意图"

图5

场馆边缘AP点误差图"

图6

人群流量统计模型"

图7

区域划分示意图"

图8

图G的邻接矩阵"

图9

热点区域轨迹预测模型"

图10

不同区域内人群移动轨迹模型"

表1

MAC存储表"

idstationMacchannelDetect ApNameRSSInoisefirstDetectTimelastDetectTime
15C:F7:C3:A2:**: **11ap203-80-1025/25/2016 00:00:005/26/2016 00:00:00
200:18:4C:04: **:**6ap207-85-915/25/2016 00:00:005/26/2016 00:00:00
348:62:76:18: **:**8ap203-85-925/25/2016 00:00:005/26/2016 00:00:00
448:62:76:18: **:**8ap203-84-925/25/2016 00:00:005/26/2016 00:00:00
5B8:B4:2E:33: **:**11ap202-66-905/25/2016 00:00:005/26/2016 00:00:00

表2

AP点信息存储表"

cs_idcs_apAliascs_macAddresscs_longcs_latcs_att
162ap3738:91:D5:B6:**:**109.*26.******
163ap33838:91:D5:B6:**:**109.*26.******
164ap31938:91:D5:B6:**:**109.*26.******
166ap34038:91:D5:B6:**:**109.*26.******
168ap2638:91:D5:B6:**:**109.*26.******

图11

数博会期间场馆人数"

图12

场馆内实时人群统计"

图13

每个展馆内实时人数统计"

图14

不同展台的人群热度分布"

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