室内定位数据分析与应用研究进展

doi:10.18306/dlkxjz.2016.05.005

Abstract

Abstract:

Human spatiotemporal behavior increasingly draws attention in modern human geography, and obtaining individuals’ spatiotemporal location data in travel activities is the precondition to study the spatiotemporal behavior of people. Limited by data acquisition technology, previous studies about behavior in space and time mostly focused on behavior in the outdoor space of a city. With the occurrence of technology for indoor positioning, the spatial scale of this type of study has been extended to the indoor space. Although there is a wide range of technologies and methods for indoor positioning, according to the manner that these data are acquired, indoor positioning data can be divided into three categories, that is, geometric location data, fingerprinting location data, and symbolic location data. Currently, studies based on indoor positioning data can be divided into the following categories: (1) spatiotemporal distribution of people in indoor space; (2) movement pattern of people in indoor space; (3) behavioral habit and attribute inference of people in indoor space; and (4) interaction between people and indoor surroundings. However, most of these studies are still at a primary stage and there are no commonly accepted theories and methodologies at present. We believe that studies in the future may pay attention to the following issues: (1) with regard to data acquisition, the positioning system accomplished through “smart phone plus WiFi” that has wider coverage and lower cost, requires no special equipment, and interacts easily compared with Bluetooth, Radio Frequency Identification (RFID), and Infrared Ray (IR),and so on, is the most promising indoor positioning technology; (2) with regard to research contents, behavioral characteristics in space and time will be the basis, individuals’ attribute inference and interaction between individuals and indoor surroundings will be the focus, and analysis of multiscale fused spatiotemporal location data will be the trend of future development; (3) in terms of scientific ethics, privacy issues must be highlighted concerning the recording of individuals’ travel activity data at the micro scale.

Key words: positioning data, indoor positioning, data analysis, spatiotemporal behavior, big data, review

Hua SHU, Ci SONG, Tao PEI. Progress of studies on indoor positioning data analysis and application[J].PROGRESS IN GEOGRAPHY, 2016, 35(5): 580-588.

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References 52

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定位方法	基本原理	系统示例(无线技术)	优缺点	应用场景	复杂度	代表性研究
三角测量法	根据待测点和坐标已知参考点之间连接形成三角形的几何特性计算待测点的坐标	RADAR(WLAN) Active Bat(Ultrasound) Ubisense(UWEB)	优点：定位精度高,理论上可实现对信号覆盖区域任意空间位置的定位; 缺点：对信号传播范围和稳定性要求较高;信号遮挡、反射和波动等会造成位置“漂移”	较空旷的室内场景(如机场、火车站大厅等)	适中	Bahl et al, 2000; Harter et al, 2002; Steggles et al, 2005
场景分析法	事先获取指定位置的信号特征参数,通过对比待测点接收到信号的特征参数与数据库中存储的各指定位置的信号特征参数,确定待测点位置	Horus(WLAN) MotionStar(Magnetic) LifeTag(WLAN)	优点：无需事先测量参考点位置坐标,定位精度高,稳定性好; 缺点：前期位置指纹数据采集工作量较大	应用场景较广泛,可用于环境复杂的室内场景	高	Youssef et al, 2004; Poulin et al, 2002; Li et al, 2006
邻近法	根据传感器信号作用范围有限的特点,确定待测点是否在参考点附近	BIPS(Bluetooth) LANDMARC(RFID) PAN(ZigBee)	优点：对传感器信号稳定性要求低,定位算法简单,实现方便; 缺点：一般需要用户主动参与,且无法记录用户空间活动轨迹的细节信息	廊道型室内场景或功能区划明确的室内场景(廊道型博物馆,商铺彼此独立的商场等)	低	Anastasi et al, 2003; Ni et al, 2003; Huang et al, 2009

Progress of studies on indoor positioning data analysis and application

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