基于相关性测序的TD-LTE分布式系统室内定位算法

李玲霞; 郭可可; 田增山; 周牧

doi:10.11999/JEIT170655

基于相关性测序的TD-LTE分布式系统室内定位算法

doi: 10.11999/JEIT170655

基金项目:

国家自然科学基金(61301126, 61471077)，长江学者和创新团队发展计划(IRT1299)，重庆市科委重点实验室专项经费，重庆市基础与前沿研究计划项目(重点)(cstc2015jcyjBX0065)，重庆市高校优秀成果转化资助项目(KJZH17117)，重庆邮电大学青年科学研究项目(A2013-31)

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出版历程
- 收稿日期: 2017-07-05
- 修回日期: 2018-01-30
- 刊出日期: 2018-05-19

TD-LTE Distributed Antenna System Based Indoor Localization Algorithm with Correlation Sequence

Funds:

The National Natural Science Foundation of China (61301126, 61471077), The Program for Changjiang Scholars and Innovative Research Team in University (IRT1299), The Special Fund of CSTC Key Laboratory, The Fundamental and Frontier Research Project of Chongqing (Key Project) (cstc2015jcyjBX0065), The University Outstanding Achievement Transformation Project of Chongqing (KJZH17117), The Young Science Research Program of Chongging University of Posts and Telecommunications (A2013-31)

摘要

摘要: 在分时长期演进(TD-LTE)室内分布式网络中，不同位置上的信号差异性不明显，仅利用标定参考点不能实现准确位置估计。该文针对TD-LTE室内分布式网络下不同位置处的信号相似性问题，提出基于相关性测序的定位算法。首先，利用相邻位置的信号信息构建运动序列数据库。其次，通过相关性测序算法，得到在线接收信号参考强度(RSRP)序列与运动序列间的匹配度，确定备选定位序列集。然后，计算备选定位序列与在线RSRP序列间的相关系数及平均欧氏距离。最后，根据匹配度、相关系数及平均欧氏距离，选取最优备选定位序列，实现对目标的位置估计。实验结果表明，该文所提定位算法有效提高了室内分布式天线系统下的定位精度。
- 室内定位 /
- 分时长期演进 /
- 分布式网络 /
- 相关性测序 /
- 运动序列数据库
Abstract: In the Time Division Long Term Evolution (TD-LTE) indoor distributed network, the signal differences among different positions are insignificant, thus accurate localization can not be achieved by reference point calibration. To solve this compelling problem, this paper proposes a correlation sequencing based localization algorithm. Firstly, the signal information among adjacent positions is utilized to construct Reference Signal Receiving Power (RSRP) motion sequence database. Next, correlation sequencing algorithm is conducted to obtain relation between real-time RSRP sequence and the ones in constructed database, which results in a set of candidate sequence. After that, the correlation coefficient and mean Euclidean distance between candidate sequences and the online one are calculated. Finally, the optimal candidate sequence is selected by a voting strategy to estimate targets position. Experimental results show that the proposed localization algorithm can effectively improve the localization accuracy within indoor distributed antenna system.
- Indoor localization /
- Time Division Long Term Evolution (TD-LTE) /
- Distributed network /
- Correlation sequencing /
- Motion sequence database

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参考文献(26)

YANG Ting and ZHU Liping. A review of modern indoor localization systems[C]. Proceedings of the 2nd National Conference on Information Technology and Computer Science, Shanghai, 2015: 10-11.

GU Y, LO A, and NIEMEGEERS I. A survey of indoor positioning systems for wireless personal networks[J]. IEEE Communications Surveys Tutorials, 2009, 11(1): 13-32. doi: 10.1109/SURV.2009.090103.

李丽娜, 马俊, 龙跃, 等. 基于LANDMARC与压缩感知的双段式室内定位算法[J]. 电子与信息学报, 2016, 38(7): 1631-1637. doi: 10.11999/JEIT151050.

LI Lina, MA Jun, LONG Yue, et al. Double stage indoor localization algorithm based on LANDMARC and compressive sensing[J]. Journal of Electronics Information Technology, 2016, 38(7): 1631-1637. doi: 10.11999/ JEIT151050.

HARTER A, HOPPER A, STEGGLES P, et al. The anatomy of a context-aware application[J]. Wireless Networks, 2002, 8(2): 187-197. doi: 10.1023/A:1013767926256.

WANG G, CHEN H, LI Y, et al. On Received-signal-strength based localization with unknown transmit power and path loss exponent[J]. IEEE Wireless Communications Letters, 2012, 1(5): 536-539. doi: 10.1109/WCL.2012.072012.120428.

JOURDAN D B, DEYST J J, WIN M Z, et al. Monte Carlo localization in dense multipath environments using UWB ranging[C]. IEEE International Conference on Ultra- Wideband, Zurich, Switzerland, 2005: 314-319. doi: 10.1109/ ICU.2005.1570005.

NI L M, LIU Y, LAU Y C, et al. LANDMARC: Indoor location sensing using active RFID[J]. Wireless Networks, 2004(10): 701-710. doi: 10.1023/B: WINE.0000044029.06344. dd.

LIU B, CHEN H, ZHONG Z, et al. Asymmetrical round trip based synchronization-free localization in large-scale underwater sensor networks[J]. IEEE Transactions on Wireless Communications, 2010, 9(11): 3532-3542. doi: 10.1109/TWC.2010.090210.100146.

ZHANG W, YIN Q, CHEN H, et al. Distributed angle estimation for localization in wireless sensor networks[J]. IEEE Transactions on Wireless Communications, 2013, 12(2): 527-537. doi: 10.1109/TWC.2012.121412.111346.

DEL PERAL-ROSADO J A, LOPEZ-SALCEDO J A, SECOGRANADOS G, et al. Evaluation of the LTE positioning capabilities under typical multipath channels[C]. IEEE Advanced Satellite Multimedia Systems Conference, Baiona, Spain, 2012: 139-146. doi: 10.1109/ASMS-SPSC. 2012. 6333065.

田增山, 王向勇, 周牧, 等. 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, et al. 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.

CHEN H, LIU B, HUANG P, et al. Mobility-assisted node localization based on TOA measurements without time synchronization in wireless sensor networks[J]. Mobile Networks Applications, 2012, 17(1): 90-99. doi: 10.1007/ s11036-010-0281-3.

WANG G and CHEN H. An importance sampling method for TDOA-based source localization[J]. IEEE Transactions on Wireless Communications, 2011, 10(5): 1560-1568. doi: 10.1109/TWC.2011.030311.101011.

HUANG B, XIE L, and YANG Z. TDOA-based source localization with distance-dependent noises[J]. IEEE Transactions on Wireless Communications, 2015, 14(1): 468-480. doi: 10.1109/TWC.2014.2351798.

YANG Congfeng and WANG Fengshuai. Joint TDOA and AOA location algorithm[J]. Journal of Systems Engineering Electronics, 2013, 24(2): 183-188. doi: 10.1109/JSEE.2013. 00023.

TIAN Zengshan, LI Ze, ZHOU Mu, et al. PILA: Sub-meter localization using CSI from commodity Wi-Fi devices[J]. Sensors, 2016, 16(10): 1664. doi: 10.3390/s16101664.

陈军. 高能效的分布式天线系统研究[D]. [博士论文], 华南理工大学, 2015.

CHEN Jun. Research on high energy-efficient distributed antenna system[D]. [Ph.D. dissertation], South China University of Technology, 2015.

ZHANG Qiulin and ZHU Xijun. Study of the thinning algorithm for thenar palmprint[C]. IEEE Computer Society, First Acis International Symposium on Cryptography, and Network Security, Data Mining and Knowledge Discovery, E-Commerce and ITS Applications, and Embedded Systems, 2010: 179-182. doi: 10.1109/CDEE.2010.100.

FENG Xingkui, LI Linyan, and YAN Zuquan. A new thinning algorithm for fingerprint image[J]. Journal of Image Graphics, 1999, 4(10): 835-838.

LAM L, LEE S W, and SUEN C Y. Thinning methodologies A comprehensive survey[J]. IEEE Transactions on Pattern Analysis Machine Intelligence, 1992, 14(9): 869-885. doi: 10.1109/34.161346.

LIU B, REN F, SHEN J, et al. Advanced self-correcting time synchronization in wireless sensor networks[J]. IEEE Communications Letters, 2010, 14(4): 309-311. doi: 10.1109/ LCOMM.2010.04.092364.

盛骤, 谢式千, 潘承毅. 概率论与数理统计[M]. 第4版, 北京: 高等教育出版社, 2008: 163-168.

SHENG Zhou, XIE Shiqian, and PAN Chengyi. Probability Theory and Mathematical Statistics[M]. 4th Ed., Beijing: Hight Educatin Press, 2008: 163-168.

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