Indoor localization via l1-graph regularized semi-supervised manifold learning

doi:10.1016/S1005-8885(11)60298-7

中国邮电高校学报(英文) ›› 2012, Vol. 19 ›› Issue (5): 39-44.doi: 10.1016/S1005-8885(11)60298-7

Indoor localization via l1-graph regularized semi-supervised manifold learning

朱宇佳¹,邓中亮²,季昊¹

1. 北京邮电大学
2. 北京邮电大学电子工程学院

收稿日期:2012-02-28 修回日期:2012-06-07 出版日期:2012-10-31 发布日期:2012-10-08
通讯作者: 朱宇佳 E-mail:zhuyjbupt@gmail.com
基金资助:
863目标导向类

Indoor localization via l1-graph regularized semi-supervised manifold learning

ZHU Yu-jia1, DENG Zhong-liang1,2, JI Hao3

1. School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China 2. Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing 100876, China 3. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2012-02-28 Revised:2012-06-07 Online:2012-10-31 Published:2012-10-08
Contact: Yu-jia ZHU E-mail:zhuyjbupt@gmail.com

摘要/Abstract

摘要：

In this paper, a new l1-graph regularized semi-supervised manifold learning (LRSML) method is proposed for indoor localization. Due to noise corruption and non-linearity of received signal strength (RSS), traditional approaches always fail to deliver accurate positioning results. The l1-graph is constructed by sparse representation of each sample with respect to remaining samples. Noise factor is considered in the construction process of l1-graph, leading to more robustness compared to traditional k-nearest-neighbor graph (KNN-graph). The KNN-graph construction is supervised, while the l1-graph is assumed to be unsupervised without harnessing any data label information and uncovers the underlying sparse relationship of each data. Combining KNN-graph and l1-graph, both labeled and unlabeled information are utilized, so the LRSML method has the potential to convey more discriminative information compared to conventional methods. To overcome the non-linearity of RSS, kernel-based manifold learning method (K-LRSML) is employed through mapping the original signal data to a higher dimension Hilbert space. The efficiency and superiority of LRSML over current state of art methods are verified with extensive experiments on real data.

关键词:

graph, indoor positioning, semi-supervised, manifold learning, wireless local area network (WLAN)

Abstract:

Key words:

graph, indoor positioning, semi-supervised, manifold learning, wireless local area network (WLAN)

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Indoor localization via l1-graph regularized semi-supervised manifold learning

Indoor localization via l1-graph regularized semi-supervised manifold learning

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