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Indoor localization via l^1-graph regularized semi-supervised manifold learning

The Journal of China Universities of Posts and Telecommunications 2012年第5期19卷 39-44,91页

作者： ZHU Yu-jia DENG Zhong-liang JI Hao School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing 100876 China Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing 100876 China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing 100876 China

In this paper, a new -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 -graph is constructed by sparse representation of each sample with respect to remaining samples. Noise factor is considered in the construction process of -graph, leading to more robustness compared to traditional k-nearest-neighbor graph （KNN-graph）. The KNN-graph construction is supervised, while the -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 -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)

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