Computer Science ›› 2018, Vol. 45 ›› Issue (10): 104-110.doi: 10.11896/j.issn.1002-137X.2018.10.020

• Network & Communication • Previous Articles     Next Articles

Adaptive Indoor Location Method for Multiple Terminals Based on Multidimensional Scaling

FU Xian-kai1,2,3, JIANG Xin-long1,2,3, LIU Jun-fa1,2,3, ZHANG Shao-bo4, CHEN Yi-qiang1,2,3   

  1. Center of Pervasive Computing System Research,Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China 1
    Beijing Key Laboratory of Mobile Computing and Pervasive Device,Chinese Academy of Sciences,Beijing 100190,China 2
    University of Chinese Academy of Sciences,Beijing 100049,China 3
    School of Information Engineering,Chang’an University,Xi’an 710064,China 4
  • Received:2018-03-21 Online:2018-11-05 Published:2018-11-05

Abstract: Indoor location is a hot research topic in the field of pervasive computing.At present,indoor location methods are mainly divided into the localization method based on signal propagation model and the one based on wireless signal fingerprint.The fingerprint based method is more widely used because it does not need to know the location of the wireless signal AP.But it needs to collect a large amount of data at the offline stage to build a rich fingerprint database,which needs a lot of manual calibration.For this reason,this paper proposed a localization method based on spatial relations of fingerprints.Compared with the traditional fingerprint localization methods,this method does not need to build a fingerprint database.It uses Wi-Fi fingerprint from multiple terminals to extract the similarity of fingerprints and construct a dissimilarity matrix,and finally applies multidimensional scaling (MDS) algorithm to construct the relative location map for all terminals.Then each terminal can be positioned by determining the position of more than 3 terminals.In this paper,support vector regression (SVR) is used to calculate the distance between arbitrary terminals,and the distance matrix is used as the dissimilarity matrix.A shopping mall which is about 2500 square meter is selected as testing environment,and the average positioning error of the proposed method is about 7 meters.

Key words: Indoor positioning, Fingerprint location method, Multidimensional scaling, SVR

CLC Number: 

  • TP311
[1]CHEUNG K W,SO H C.A multidimensional scaling framework for mobile location using time-of-arrival measurements[J].IEEE Transactions on Signal Processing,2005,53(2):460-470.
[2]BAHL P,PADMANABHAN V N.RADAR:An In-Building RF-based User Location and Tracking System[C]∥Proc. IEEE Infocom.2000:775-784.
[3]MAI A A,ALHADHRAMI S,ALSALMAN A,et al.Comparative Survey of Indoor Positioning Technologies,Techniques,and Algorithms[C]∥International Conference on Cyberworlds.IEEE,2014:245-252.
[4]FARID Z,NORDIN R,ISMAIL M.Recent advances in wireless indoor localization techniques and system[J].Journal of Computer Networks and Communications,2013,2013(42):15.
[5]GU Y,LO A,NIEMEGEERS I.A survey of indoor positioning systems for wireless personal networks[J].IEEE Communications Surveys & Tutorials,2009,11(1):13-32.
[6]HAN T,LU X,LAN Q.Pattern recognition based Kalman filter for indoor localization using TDOA algorithm [J].Applied Mathematical Modelling,2010,34(10):2893-2900.
[7]NICULESCU D,NATH B.VOR base stations for indoor 802.11 positioning[C]∥International Conference on Mobile Computing and Networking.DBLP,2004:58-69.
[8]KIM B,BONG W,KIM Y C.Indoor localization for Wi-Fi devices by cross-monitoring AP and weighted triangulation[C]∥Consumer Communications and NETWORKING Conference.IEEE,2011:933-936.
[9]KAEMARUNGSI K,KRISHNAMURTHY P.Properties of Indoor Received Signal Strength for WLAN Location Fingerprin-ting[C]∥International Conference on Mobile and Ubiquitous Systems:NETWORKING and Services,2004.DBLP,2004:14-23.
[10]KAEMARUNGSI K,KRISHNAMURTHY P.Analysis of WLAN’s received signal strength indication for indoor location fingerprinting[J].Pervasive & Mobile Computing,2012,8(2):292-316.
[11]PEI L,GUINNESS R,CHEN R,et al.Human Behavior Cogni- tion Using Smartphone Sensors[J].Sensors,2013,13(2):1402.
[12]WANG F,HUANG Z,YU H,et al.EESM-based fingerprint algorithm for Wi-Fi indoor positioning system[C]∥IEEE/CIC International Conference on Communications in China.IEEE,2013:674-679.
[13]LIN T N,LIN P C.Performance comparison of indoor positioning techniques based on location fingerprinting in wireless networks[C]∥International Conference on Wireless Networks,Communications and Mobile Computing.IEEE Xplore,2005:1569-1574.
[14]KOO J,CHA H.Autonomous construction of a Wi-Fi access point map using multidimensional scaling[C]∥Pervasive Computing,International Conference.DBLP,2011:115-132.
[15]JI X,ZHA H.Sensor positioning in wireless ad-hoc sensor networks using multidimensional scaling[C]∥Joint Conference of the IEEE Computer and Communications Societies.IEEE Xplore,2004:2652-2661.
[16]SECTION.D3D-MDS:A Distributed 3D Localization Scheme for an Irregular Wireless Sensor Network Using Multidimensional Scaling[J].International Journal of Distributed Sensor Networks,2015,2015(7):1-10.
[17]OSUNA E,FREUND R,GIROSI F.An improved training algorithm for support vector machines[C]∥Neural Networks for Signal Processing VII-Proceedings of the 1997 IEEE Workshop.1997:276-285.
[1] CHEN Shi-jun, WANG Hui-qiang, WANG Yuan-yuan, HU Hai-jing. Base Station Selection Optimization Method Oriented at Indoor Positioning [J]. Computer Science, 2018, 45(10): 115-119, 159.
[2] QIN Xu-jia, SHAN Yang-yang, XIAO Jia-ji, ZHENG Hong-bo and ZHANG Mei-yu. Self-learning Single Image Super-resolution Reconstruction Based on Compressive Sensing and SVR [J]. Computer Science, 2017, 44(Z11): 169-174, 188.
[3] HUANG Xu, FAN Jing, WU Mao-nian and GU Yong-gen. Design and Implementation of Intelligent Parking System Based on Wi-Fi Fingerprint Location Technology [J]. Computer Science, 2016, 43(Z6): 512-515, 541.
[4] TANG Yang, BAI Yong, MA Yue and LAN Zhang-li. Research of WiFi-based Fingerprinting Matching Algorithm in Indoor Positioning [J]. Computer Science, 2016, 43(5): 73-75.
[5] LU Yin and MIAO Hui-hui. Study on WiFi Location Technology under Complex Indoor Environment [J]. Computer Science, 2016, 43(11): 152-154.
[6] WANG Pei-zhong, ZHENG Nan-shan and ZHANG Yan-zhe. Indoor Positioning Algorithm Based on Dynamic K Value and AP MAC Address Match [J]. Computer Science, 2016, 43(1): 163-165.
[7] LIU Ding-jun, JIANG Xin-long, LIU Jun-fa and CHEN Yi-qiang. Multi-sensor Fusion Based Indoor Real-time and Highly Accurate Trajectory Generation [J]. Computer Science, 2016, 43(1): 18-21, 43.
[8] LEI Ying-ke. New Fast Manifold Learning Algorithm Based on MSC and ISOMAP [J]. Computer Science, 2015, 42(8): 244-248.
[9] CUI Jin-qi and TAO Xian-ping. Design and Implementation of RFID-based Campus Navigation System [J]. Computer Science, 2015, 42(12): 92-94, 119.
[10] ZHANG Wei and SUN Qiang. One of Indoor Positioning Algorithm Based on Wireless Sensor Network [J]. Computer Science, 2014, 41(Z11): 232-234.
[11] CAI Zhao-hui,XIA Xi,HU Bo and FAN Dan-mei. Improvements of Indoor Signal Strength Fingerprint Location Algorithm [J]. Computer Science, 2014, 41(11): 178-181.
[12] WANG Wen-chao,MIAO Duo-qian and CHEN Ji-yuan. Gas Turbine Power Prediction Based on Support Vector Regression [J]. Computer Science, 2013, 40(Z6): 368-371.
[13] WEN Dong-qin,WANG Jian-dong and ZHANG Xia. Prediction Model for Airport-noise Time Series Based on GM-LSSVR [J]. Computer Science, 2013, 40(9): 198-200,220.
[14] XIE Dai-jun,HU Han-ying and KONG Fan-zeng. Indoor Positioning Algorithm for WLAN Based on Distribution Overlap and Feature Weighting [J]. Computer Science, 2013, 40(11): 38-42.
[15] . Self-adjusting Distance MDS Algorithm for WSNs [J]. Computer Science, 2012, 39(5): 40-43,47.
Full text



[1] . [J]. Computer Science, 2018, 1(1): 1 .
[2] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75, 88 .
[3] XIA Qing-xun and ZHUANG Yi. Remote Attestation Mechanism Based on Locality Principle[J]. Computer Science, 2018, 45(4): 148 -151, 162 .
[4] LI Bai-shen, LI Ling-zhi, SUN Yong and ZHU Yan-qin. Intranet Defense Algorithm Based on Pseudo Boosting Decision Tree[J]. Computer Science, 2018, 45(4): 157 -162 .
[5] WANG Huan, ZHANG Yun-feng and ZHANG Yan. Rapid Decision Method for Repairing Sequence Based on CFDs[J]. Computer Science, 2018, 45(3): 311 -316 .
[6] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[7] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[8] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[9] LIU Qin. Study on Data Quality Based on Constraint in Computer Forensics[J]. Computer Science, 2018, 45(4): 169 -172 .
[10] ZHONG Fei and YANG Bin. License Plate Detection Based on Principal Component Analysis Network[J]. Computer Science, 2018, 45(3): 268 -273 .