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基于DBSCAN子空间匹配的蜂窝网室内指纹定位算法

田增山 王向勇 周牧 李玲霞

田增山, 王向勇, 周牧, 李玲霞. 基于DBSCAN子空间匹配的蜂窝网室内指纹定位算法[J]. 电子与信息学报, 2017, 39(5): 1157-1163. doi: 10.11999/JEIT160768
引用本文: 田增山, 王向勇, 周牧, 李玲霞. 基于DBSCAN子空间匹配的蜂窝网室内指纹定位算法[J]. 电子与信息学报, 2017, 39(5): 1157-1163. doi: 10.11999/JEIT160768
TIAN Zengshan, WANG Xiangyong, ZHOU Mu, LI Lingxia. DBSCAN Based Subspace Matching for Indoor Cellular Network Fingerprint Positioning Algorithm[J]. Journal of Electronics and 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 and Information Technology, 2017, 39(5): 1157-1163. doi: 10.11999/JEIT160768

基于DBSCAN子空间匹配的蜂窝网室内指纹定位算法

doi: 10.11999/JEIT160768
基金项目: 

国家自然科学基金(61301126),长江学者和创新团队发展计划(IRT1299),重庆市基础与前沿研究计划(cstc2013jcyjA 40041, cstc2015jcyjBX0065),重庆邮电大学青年科学研究项目(A2013-31)

DBSCAN Based Subspace Matching for Indoor Cellular Network Fingerprint Positioning Algorithm

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)

  • 摘要: 针对无线信道动态衰落特性引起的蜂窝网室内定位误差较大的问题,该文提出基于密度的空间聚类(Density Based Spatial Clustering of Applications with Noise, DBSCAN)子空间匹配算法,有效剔除大误差点,提高定位精度。首先通过划分信号空间,构建多个子空间,在子空间中利用加权K近邻匹配算法(Weighted K Nearest Neighbor, WKNN)估计出目标位置;然后利用DBSCAN对估计位置进行聚类以剔除异常点;最后结合概率模型确定最终估计位置。实验结果表明,基于DBSCAN的子空间匹配算法能有效剔除大误差点,提高蜂窝网室内定位系统的整体性能。
  • 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.
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出版历程
  • 收稿日期:  2016-07-22
  • 修回日期:  2016-12-27
  • 刊出日期:  2017-05-19

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