基于流形插值数据库构建的WLAN室内定位算法

周牧; 唐云霞; 田增山; 卫亚聪

doi:10.11999/JEIT161269

基于流形插值数据库构建的WLAN室内定位算法

doi: 10.11999/JEIT161269

2.
(重庆邮电大学移动通信技术重庆市重点实验室重庆 400065) ②(天津师范大学天津市无线移动通信与无线电能传输重点实验室天津 300387)

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)，重庆市科委重点实验室专项经费，重庆邮电大学青年科学研究项目(A2013-31)

计量
- 文章访问数: 1511
- HTML全文浏览量: 212
- PDF下载量: 393
- 被引次数: 0
出版历程
- 收稿日期: 2016-11-24
- 修回日期: 2017-03-20
- 刊出日期: 2017-08-19

WLAN Indoor Localization Algorithm Based on Manifold Interpolation Database Construction

2.
(Chongqing Key Laboratory of Mobile Communication Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China)

Funds:

The National Natural Science Foundation of China (61301126), The Program for Changjiang Scholars and Innovative Research Team in University (IRT1299), The Special Fund of Chongqing Key Laboratory (CSTC), Young Scientific Research Program of CUPT (A2013-31)

摘要

摘要: 针对传统无线局域网(WLAN)室内定位系统中因参考点密集分布及逐点信号采集所带来的位置指纹数据库构建工作量繁重的问题，该文提出一种基于混合半监督流形学习和3次样条插值的数据库构建方法。该方法利用少量标记数据和大量未标记数据求解定位目标函数的最优解，同时根据高维信号强度空间与低维物理位置空间的映射关系，实现对未标记数据的位置标定。大量实验结果表明，该方法能够在保证较高定位精度的同时，显著降低位置指纹数据库的构建开销。
- 无线局域网 /
- 位置指纹 /
- 半监督学习 /
- 流形对齐 /
- 3次样条插值
Abstract: To deal with the high cost involved in the location fingerprint database construction due to the dense Reference Points (RPs) distribution and point-by-point Received Signal Strength (RSS) collection in the conventional Wireless Local Area Network (WLAN) indoor localization systems, a new database construction approach based on the integrated semi-supervised manifold learning and cubic spline interpolation is proposed. The proposed approach utilizes a small amount of labeled data and a massive amount of unlabeled data to find the optimal solution to localization target function, and meanwhile relies on the mapping relations between the high-dimensional signal strength space and low-dimensional physical location space to calibrate the unlabeled data with location coordinates. The extensive experiments demonstrate that the proposed approach is able to guarantee the high localization accuracy, as well as significantly reduce the cost involved in location fingerprint database construction.
- Wireless Local Area Network (WLAN) /
- Location fingerprint /
- Semi-supervised learning /
- Manifold alignment /
- Cubic spline interpolation

HTML全文

参考文献(26)

KASHIF A, TAYYAB J, HOSSAM S H, et al. Non-audible acoustic communication and its application in indoor location-based services[C]. IEEE Wireless Communications and Networking Conference, Doha, Qatar, 2016: 1-6.

周牧, 蒲巧林, 田增山. 室内WLAN定位中位置指纹优化的接入点部署方法[J]. 通信学报, 2015, 36(Z1): 30-41. doi: 10.11959/j.issn.1000-436x.2015279.

ZHOU Mu, PU Qiaolin, and TIAN Zengshan. Location fingerprint optimization based access point deployment in indoor WLAN localization[J]. Journal on Communications, 2015, 36(Z1): 30-41. doi: 10.11959/j.issn.1000-436x.2015279.

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.

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

TAPONECCO L, AMICO A D, and MENGALI U. Joint TOA and AOA estimation for UWB localization applications [J]. IEEE Transactions on Wireless Communications, 2011, 10(7): 2207-2217. doi: 10.1109/TWC.2011.042211.100966.

ZHANG Liye, MA Lin, and XU Yubin. A semi-supervised indoor localization method based on l1-graph algorithm[J]. Journal of Harbin Institute of Technology (New Series), 2015, 22(4): 55-61.

OUYANG R, WONG A, LEA C, et al. Indoor location estimation with reduced calibration exploiting unlabeled data via hybrid generative/discriminative learning[J]. IEEE Transactions on Mobile Computing, 2011, 11(11): 1613-1626. doi: 10.1109/TMC.2011.193.

PAN J J, PAN S J, YIN J, et al. Tracking mobile users in wireless networks via semi-supervised colocalization[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(3): 587-600. doi: 10.1109/TPAMI.2011.165.

励晶晶, 郭文. 两类错误条件下的样本容量选择[J]. 统计与决策, 2010, (15): 14-18.

LI Jingjing and GUO Wen. Select the sample size under two types of error conditions[J]. Statistics and Decision, 2010, (15): 14-18.

贾俊平. 统计学[M]. 北京. 中国人民大学出版社. 2011: 220-224.

JIA Junping. Statistics[M]. Beijing, China Renmin University Press, 2011: 220-224.

彭小辉, 晏政, 李艳军, 等. 一种基于解析冗余关系的半定性故障隔离方法在航天器推进系统中的应用[J]. 国防科技大学学报. 2012, 34(6): 104-110. doi: 10.3969/j.issn.1001-2486. 2012.06.018.

PENG Xiaohui, YAN Zheng, LI Yanjun, et al. A semi- qualitative fault isolation method based on analytical redundancy relations for spacecraft propulsion system[J]. Journal of National University of Defense Technology, 2012, 34(6): 104-110. doi: 10.3969/j.issn.1001-2486.2012.06.018.

陈家琪, 严梓乘. 一种Newton插值的RFID室内定位改进算法[J]. 计算机系统应用, 2012, (1): 45-48. doi: 10.3969/j.issn. 1003-3254.2012.01.010.

CHEN Jiaqi and YAN Zicheng. Improvement algorithm of Newton interpolation for RFID indoor positioning[J]. Computer Systems Applications, 2012, (1): 45-48. doi: 10. 3969/j.issn.1003-3254.2012.01.010.

BELKIN M and NIYOGI P. Laplacian eigenmaps for dimensionality reduction and data representation[J]. Neural Computation, 2003, 15(6): 1373-1396. doi: 10.1162/ 089976603321780317.

SHI Lei, ZHANG Lefei, ZHAO Lingli, et al. Adaptive Laplacian eigenmap-based dimension reduction for ocean target discrimination[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(7): 902-906. doi: 10.1109/LGRS. 2016.2553046.

ZHANG Yanming, HUANG Kaizhu, HOU Xinwen, et al. Learning locality preserving graph from data[J]. IEEE Transactions on Cybernetics, 2014, 44(11): 2088-2098. doi: 10.1109/TCYB.2014.2300489.

曾孝平, 刘刈, 刘国金. 基于图谱理论和随机游走核的图像去噪[J]. 通信学报. 2010, 31(7): 116-121. doi: 10.3969/j.issn. 1000-436X.2010.07.017.

ZENG Xiaoping, LIU Yi, and LIU Guojin. Image denoising based on spectral graph theory and random walk kernel[J]. Journal on Communications, 2010, 31(7): 116-121. doi: 10.3969/j.issn.1000-436X. 2010. 07.017.

金珠. 改进的支持向量机分类算法及其在煤矿人因事故安全评价中的应用[D]. [博士论文], 中国矿业大学, 2011, 81-89.

JIN Zhu. Improved support vector machine classification algorithm and application for human factors of coal mine accidents safety evaluation[D]. [Ph.D. dissertation], China University of Mining, 2011, 81-89.

RENAN S, PASCAL D C, RICHARD P, et al. Human step detection from a piezoelectric polymer floor sensor using normalization algorithms[C]. IEEE Sensors 2014, Valencia, Spain, 2014: 1169-1172.

施引文献

资源附件(0)

访问统计