[1]何富贵,杨铮,吴陈沭,等.一种层次Levenshtein距离的无指纹校准的室内定位方法[J].智能系统学报,2017,12(3):422-429.[doi:10.11992/tis.201704031]
HE Fugui,YANG Zheng,WU Chenshu,et al.An fingerprint calibrations-free indoor localization method based on hierarchical Levenshtein distance[J].CAAI Transactions on Intelligent Systems,2017,12(3):422-429.[doi:10.11992/tis.201704031]
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HE Fugui,YANG Zheng,WU Chenshu,et al.An fingerprint calibrations-free indoor localization method based on hierarchical Levenshtein distance[J].CAAI Transactions on Intelligent Systems,2017,12(3):422-429.[doi:10.11992/tis.201704031]
一种层次Levenshtein距离的无指纹校准的室内定位方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
12
期数:
2017年第3期
页码:
422-429
栏目:
学术论文—智能系统
出版日期:
2017-06-25
- Title:
- An fingerprint calibrations-free indoor localization method based on hierarchical Levenshtein distance
- 关键词:
- 室内定位; WiFi指纹; 设备异构; 无指纹校准; Levenshtein距离
- Keywords:
- indoor localization; WiFi fingerprint; heterogeneous device; fingerprint calibration-free; Levenshtein distance
- 分类号:
- TP181
- 摘要:
- 随着移动计算领域的兴起，基于位置的服务越来越受青睐。目前各种室内定位的方法层出不穷，由于室内广泛部署了无线基础设施，基于WiFi指纹信息的室内定位技术是其主流方法。设备异构和室内环境变化是影响定位精度的主要因素。本文针对以上两个问题，提出一种层次Levenshtein距离（HLD）的WiFi指纹距离计算算法，实现异构设备的指纹无校准比对。将不同移动设备采集的RSSI信息转化为AP序列，根据AP对应的RSSI值的差异性计算其层次能级，结合Levenshtein距离计算WiFi指纹之间的距离。对于需定位的WiFi指纹RSSI信息，利用HLD算法获取K个近邻，采用WKNN算法进行预测定位。实验中，为了验证算法的鲁棒性和有效性，在3种不同类型的室内环境中采用5种不同的移动设备来采集WiFi的RSSI信息，其定位的平均精度达1.5 m。
- Abstract:
- In the era of mobile computing, location-based services have become extremely important for a wide range of applications, and various wireless indoor localization techniques have been emerging. Amongst these techniques, WiFi fingerprint-based indoor localization is one of the most attractive because of the wide deployment and availability of WiFi infrastructure. The accuracy of indoor localization is affected by two main factors: equipment heterogeneity and environmental dynamics. To solve the obove two problems, an algorithm based on hierarchical Levenshtein distance (HLD) was proposed to realize calibration-free fingerprint comparison of heterogeneous devices. Received signal strength indication(RSSI) information collected via different mobile devices was transformed into an AP sequence. The difference in the Received signal strength indication RSSI values was used to calculate the hierarchical energy level of each access point(AP). Next, the distance between the WiFi fingerprints was calculated using the Levenshtein distance. To locate WiFi fingerprint RSSI information, the HLD algorithm was used to obtain K neighbors and the weighted K nearest neighbor(WKNN) algorithm was used to predict its position. Five different mobile devices were used to collect WiFi RSSI information in three different types of indoor environments to verify the robustness and effectiveness of the algorithm. The average localization accuracy was 1.5 m.
- 参考文献/References:
-
[1] GU Y, LO A, NIEMEGEERS I. A survey of indoor positioning systems for wireless personal networks[J]. IEEE commun. surveys and tutorials, 2009,11(1): 13-32.
[2] HARLE R. A survey of indoor inertial positioning systems for pedestrians[J]. IEEE commun. surveys & tutorials, 2013,15(3): 1281-1293.
[3] SUBBU K P. Analysis and status quo of smart-phone-based indoor localization systems[J]. IEEE wireless commun, 2014,21(4): 106-112.
[4] 石柯,陈洪生,张仁同.一种基于支持向量回归的802.11无线室内定位方法[J].软件学报,2014,25(11): 2636-2651.SHI Ke, CHEN Hongsheng, ZHANG Rentong. Indoor location method based on support vector regression in 802.11 wireless environments[J]. Journal of software, 2014,25(11): 2636-2651.
[5] BAHL P, PADMANABHAN V. RADAR: an in-building rf-based user location and tracking system[C]//Proc. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies. Tel Aviv, Israel, 2000:775-784.
[6] YOUSSEF M, AGRAWALA A . The horus wlan location determination system[C]//Proceedings of the 3rd International Conferenceon Mobile Systems, Applications, and Services,Washington, USA, 2005: 205-218.
[7] WANG B, CHEN Q, YANG L T, et al. Indoor smartphone localization via fingerprint crowdsourcing: challenges and approaches[J]. IEEE wireless communication, 2016(6):82-89.
[8] TSUI A W, CHUANG Y H, CHU H H. Unsupervised learning for solving rss hardware variance problem in wifi localization[J].Mobile networks and applications, 2009,14(5): 677-691.
[9] CHENG H, WANG F, TAO R, et al. Clustering algorithms research for device-clustering localization[C]//2012 International Conference on Indoor Positioning and Indoor Navigation (IPIN),Sydney, Australia, 2012:1-7.
[10] MAHTAB HOSSAIN A, JIN Y,SOH W S, et al. SSD: a robust RF location fingerprint addressing mobile devices heterogeneity[J]. IEEE transactions on mobile computing, 2013,12(1): 65-77.
[11] PARK J G, CURTIS D, TELLER S, et al. Implications of device diversity for organic localization[C]//The 30th IEEE International Conference on Computer Communications, Shanghai, China, 2011:3182-3190.
[12] FIGUERA C, ROJO-LVAREZ J L, MORA-JIMNEZ I, et al. Time-space sampling and mobile device calibration for wifi indoor location systems[J]. IEEE transactions on mobile computing, 2011,10(7):913-926.
[13] HAEBERLEN A, FLANNERY E, LADD A M, et al. Practical robust localization over large-scale 802.11 wireless networks[C]//Proceedings of the 10th Annual International Conference on Mobile Computing and Networking, Philadelphia, USA, 2004: 70-84.
[14] KJRGAARD M B. Indoor location fingerprinting with heterogeneous clients[J]. Pervasive and mobile computing, 2011, 7(1):31-43.
[15] DELLA ROSA F, LEPPAKOSKI H, BIANCULLO S, et al. Ad-hoc networks aiding indoor calibrations of heterogeneous devices for fingerprinting applications[C]//2010 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Zurich, Switzerland, 2010: 1-6.
[16] CHEN L H, WU E H K, JIN M H, et al. Homogeneous features utilization to address the device heterogeneity problem in fingerprint localization[J]. IEEE sensors journal, 2014,14(4): 998-1005.
[17] ZOU H, LU X, JIANG H,et al. A fast and precise indoor localization algorithm based on an online sequential extreme learning machine[J]. Sensors, 2015,15(1): 1804-1824.
[18] LYMBEROPOULOS D, LIU J, YANG X, et al. A realistic evaluation and comparison of indoor location technologies: experiences and lessons learned[C] //Proceedings of the 14th International Conference on Information Processing in Sensor Networks, Catania, Italy, ACM, 2015: 178-189.
[19] YANG S. Freeloc: calibration-free crowdsourced indoor localization[C]//The 32th IEEE International Conference on Computer Communications, Turin, Italy, 2013: 2481-2489.
[20] JIANG Y. Ariel: automatic wi-fi based room fingerprinting for indoor localization[C]//Proc ACM Conf. Ubiquitous Computing, Pittsburgh, Pennsylvania, United States, 2012:441-50.
[21] SHU Y, HUANG Y, ZHANG J, et al. Gradient-based fingerprinting for indoor localization and tracking[J]. IEEE transactions on industrial electronics, 2016,63(4):2424-2433.
[22] ZOU H, HUANG B, LU X, et al. Standardizing location fingerprints across heterogeneous mobile devices for indoor localization[C]//IEEE Wireless Communications and Networking Conference (WCNC 2016). Doha, Qatar, 2016:1-6.
[23] GU Y, CHEN M, REN F, et al. HED: handling environ-mental dynamics in indoor wifi fingerprint localization[C]//IEEE Wireless Communications and Networking Conference (WCNC 2016), Doha, Qatar, 2016: 5-10.
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备注/Memo
收稿日期:2017-04-23。
基金项目:国家自然科学基金项目（61572366，61303209，61522110，61402 006，61673020）；2016年安徽省高校优秀中青年骨干人才国内外访学研修重点项目（gxfxZD2016190）；安徽大学信息保障技术协同创新中心2015年度开放课题（ADXXBZ201504）.
作者简介:何富贵,男,1982年生,副教授,主要研究方向为移动计算、室内定位和粒计算。发表学术论文10余篇;杨铮,男,1983年生,副教授,博士生导师,研究方向为无线网络与移动计算,包括传感网、Mesh网络、室内定位、群智感知等。发表论文60余篇,其中CCF推荐A类论文40余篇;出版中、英文学术专著各1部。获得国家自然科学奖二等奖;吴陈沭,男,1989年生,博士,研究方向为无线网络与移动计算,包括室内定位、群智感知等。
通讯作者:何富贵.E-mail:fuguihe@163.com.
更新日期/Last Update:
2017-06-25