DBSCAN Based Subspace Matching for Indoor Cellular Network Fingerprint Positioning Algorithm

TIAN Zengshan; WANG Xiangyong; ZHOU Mu; LI Lingxia

doi:10.11999/JEIT160768

Volume 39 Issue 5

May 2017

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(5): 1157-1163

TIAN Zengshan, WANG Xiangyong, ZHOU Mu, LI Lingxia. DBSCAN Based Subspace Matching for Indoor Cellular Network Fingerprint Positioning Algorithm[J]. Journal of Electronics & Information Technology, 2017, 39(5): 1157-1163. doi: 10.11999/JEIT160768

Citation:

TIAN Zengshan, WANG Xiangyong, ZHOU Mu, LI Lingxia. DBSCAN Based Subspace Matching for Indoor Cellular Network Fingerprint Positioning Algorithm[J]. Journal of Electronics & Information Technology, 2017, 39(5): 1157-1163. doi: 10.11999/JEIT160768

Citation:

PDF( 292 KB)

DBSCAN Based Subspace Matching for Indoor Cellular Network Fingerprint Positioning Algorithm

doi: 10.11999/JEIT160768

Funds:

The National Natural Science Foundation of China (61301126), The Program for Changjiang Scholars and Innovative Research Team in University (IRT1299), The Fundamental and Frontier Research Project of Chongqing (cstc2013jcyjA40041, cstc2015jcyjBX0065), The Young Science Research Program of Chongging University of Posts and Telecommunications (A2013-31)

Received Date: 2016-07-22
Rev Recd Date: 2016-12-27
Publish Date: 2017-05-19

Abstract

Abstract

For the sake of reducing the indoor localization errors caused by dynamic signal fading in cellular network, this paper propose a novel Density-Based Spatial Clustering of Applications with Noise (DBSCAN) based subspace matching algorithm for indoor localization, which can improve the localization accuracy by eliminating the location with large errors. Specifically, the signal space is firstly divided into several subspaces, where a position estimation can be obtained respectively using the Weighted K Nearest Neighbors (WKNN) approach. Then, DBSCAN is applied to the position coordinates obtained from each subspace which eliminates the outliers. Finally, the location is estimated based on probability analysis. Experimental results show that the proposed approach can improve the location accuracy by eliminating the location with large errors.
- Indoor localization,
- Cellular networks,
- Density Based Spatial Clustering Applications with Noise (DBSCAN),
- Subspace matching

FullText(HTML)

References(21)

References

THI H N V, KEUN H R, and NAMKYU P. A method for predicting future location of mobile user for location-based services system[J]. Computers Industrial Engineering, 2009, 57(1): 91-105. doi: 10.1016/j.cie.2008.07.009.

KUNG H Y, CHAISIT S, and PHUONG N T M. Optimization of an RFID location identification scheme based on the neural network[J]. International Journal of Communication Systems, 2015, 28(4): 625-644. doi: 10.1002/ dac.2692.

吴楠, 王旭东, 胡晴晴, 等. 基于多LED的高精度室内可见光定位方法[J]. 电子与信息学报, 2015, 37(3): 727-732. doi: 10.11999/JEIT140725.

WU Nan, WANG Xudong, HU Qingqing, et al. Multiple LED based high accuracy indoor visible light positioning scheme[J]. Journal of Electronics Information Technology, 2015, 37(3): 727-732. doi: 10.11999/JEIT140725.

WANG Yixin, YE Qiang, CHENG Jie, et al. RSSI-based bluetooth indoor localization[C]. International Conference on Mobile Ad-hoc and Sensor Networks (MSN), Shenzhen, 2015: 165-171.

YANG Bo, LEI Yiqun, and YAN Bei. Distributed multi-human location algorithm using naive bayes classifier for a binary pyroelectric infrared sensor tracking system[J]. IEEE Sensors Journal, 2015, 16(1): 216-223. doi: 10.1109/ JSEN. 2015.2477540.

陈兵, 杨小玲. 一种基于概率密度的WLAN接入点定位的算法[J]. 电子与信息学报, 2015, 37(4): 855-862. doi: 10.11999/ JEIT140661.

CHEN Bing and YANG Xiaoling. A WLAN access point localization algorithm based on probability density[J]. Journal of Electronics Information Technology, 2015, 37(4): 855-862. doi: 10.11999/JEIT140661.

WILLEMSEN T, KELLER F, and STERNBERG H. Concept for building a MEMS based indoor localization system[C]. International Conference on Indoor Positioning and Indoor Navigation (IPIN), Busan, 2014: 1-10.

WANG Jie, GAO Qinghua, YU Yan, et al. Toward robust indoor localization based on Bayesian filter using chirp-spread-spectrum ranging[J]. IEEE Transactions on Industrial Electronics, 2012, 59(3): 1622-1629.

WANG Jie, GAO Qinghua, WANG Hongyu, et al. Device-free localization with multi-dimensional wireless link information[J]. IEEE Transactions on Vehicular Technology, 2015, 64(1): 356-366.

HE Jie, LI Shen, PAHLAVAN Kaveh, et al. A realtime testbed for performance evaluation of indoor TOA location system[C]. 2012 IEEE International Conference on Communications (ICC), Ottawa, 2012: 482-486.

HARA Shinsuke, ANZAI Daisuke, YABU Tomofumi, et al. Analysis on TOA and TDOA location estimation performances in a cellular system[C]. 2011 IEEE International Conference on Communications (ICC), Kyoto, 2011: 1-5.

LIU Congfeng, YANG Jie, and WANG Fengshuai. Joint TDOA and AOA location algorithm[J]. Journal of Systems Engineering and Electronics, 2013, 24(2): 183-188. doi: 10.1109/JSEE.2013.00023.

TIAN Zengshan, LIU Xindi, ZHOU Mu, et al. Mobility tracking by fingerprint-based KNN/PF approach in cellular networks[C]. 2013 IEEE Wireless Communications and Networking Conference (WCNC), Shanghai, 2013: 4570-4575.

MAHER P S and MALANEY R A. A novel fingerprint location method using ray-tracing[C]. Global Telecommunications Conference (GLOBECOM), Honolulu, 2009: 1-5.

DRAWIL N, AMAR H, and BASIR O. Cellular network fingerprint localization simulation: A soft computing approach[C]. 2014 IEEE 80th Vehicular Technology Conference, Vancouver, 2014: 1-5.

范明, 孟晓峰. 数据挖掘概念与技术[M]. 第3版, 北京: 机械工业出版社, 2012: 307-309.

FAN Ming and MENG Xiaofeng. Data Minging Concepts and Techniques[M]. Third Edition, Beijing, China Machine Press, 2012: 307-309.

HONG J and OHTSUKI T. Device-free passive localization from signal subspace eigenvectors[C]. 2014 IEEE Global Communications Conference, Austin, 2014: 430-435.

DING Genming, TAN Zhenhui, ZHANG Jinbao, et al. Regional propagation model based fingerprinting localization in indoor environments[C]. IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, London, 2013: 291-295.

Relative Articles

Supplements(0)

Cited By

Proportional views