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基于流形插值數(shù)據(jù)庫構(gòu)建的WLAN室內(nèi)定位算法

周牧,  唐云霞,  田增山,  衛(wèi)亞聰

周牧, 唐云霞, 田增山, 衛(wèi)亞聰. 基于流形插值數(shù)據(jù)庫構(gòu)建的WLAN室內(nèi)定位算法[J]. 電子與信息學(xué)報, 2017, 39(8): 1826-1834. doi: 10.11999/JEIT161269
引用本文: 周牧, 唐云霞, 田增山, 衛(wèi)亞聰. 基于流形插值數(shù)據(jù)庫構(gòu)建的WLAN室內(nèi)定位算法[J]. 電子與信息學(xué)報, 2017, 39(8): 1826-1834. doi: 10.11999/JEIT161269
ZHOU Mu, TANG Yunxia, TIAN Zengshan, WEI Yacong. WLAN Indoor Localization Algorithm Based on Manifold Interpolation Database Construction[J]. Journal of Electronics & Information Technology, 2017, 39(8): 1826-1834. doi: 10.11999/JEIT161269
Citation: ZHOU Mu, TANG Yunxia, TIAN Zengshan, WEI Yacong. WLAN Indoor Localization Algorithm Based on Manifold Interpolation Database Construction[J]. Journal of Electronics & Information Technology, 2017, 39(8): 1826-1834. doi: 10.11999/JEIT161269

基于流形插值數(shù)據(jù)庫構(gòu)建的WLAN室內(nèi)定位算法

doi: 10.11999/JEIT161269
  • 2.

    (重慶郵電大學(xué)移動通信技術(shù)重慶市重點(diǎn)實(shí)驗(yàn)室 重慶 400065) ②(天津師范大學(xué)天津市無線移動通信與無線電能傳輸重點(diǎn)實(shí)驗(yàn)室 天津 300387)

基金項目: 

國家自然科學(xué)基金(61301126)，長江學(xué)者和創(chuàng)新團(tuán)隊發(fā)展計劃(IRT1299)，重慶市科委重點(diǎn)實(shí)驗(yàn)室專項經(jīng)費(fèi)，重慶郵電大學(xué)青年科學(xué)研究項目(A2013-31)

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: 針對傳統(tǒng)無線局域網(wǎng)(WLAN)室內(nèi)定位系統(tǒng)中因參考點(diǎn)密集分布及逐點(diǎn)信號采集所帶來的位置指紋數(shù)據(jù)庫構(gòu)建工作量繁重的問題，該文提出一種基于混合半監(jiān)督流形學(xué)習(xí)和3次樣條插值的數(shù)據(jù)庫構(gòu)建方法。該方法利用少量標(biāo)記數(shù)據(jù)和大量未標(biāo)記數(shù)據(jù)求解定位目標(biāo)函數(shù)的最優(yōu)解，同時根據(jù)高維信號強(qiáng)度空間與低維物理位置空間的映射關(guān)系，實(shí)現(xiàn)對未標(biāo)記數(shù)據(jù)的位置標(biāo)定。大量實(shí)驗(yàn)結(jié)果表明，該方法能夠在保證較高定位精度的同時，顯著降低位置指紋數(shù)據(jù)庫的構(gòu)建開銷。
    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.
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出版歷程
  • 收稿日期:  2016-11-24
  • 修回日期:  2017-03-20
  • 刊出日期:  2017-08-19

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