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Improved Graph-Based Semi-Supervised Learning for Fingerprint-Based Indoor Localization

In this paper, a real WiFi fingerprint based indoor localization system is considered for experiments, including three primary components: the APP in the smart phone, the server system and the embedded localization algorithm. As we all know, one of the main drawbacks in fingerprint based localization is the labor intensity and time consumption of data collection. This paper proposes an improved graph-based semi-supervised learning (I-GSSL) to better overcome this problem. Apart from taking advantage of the indoor propagation model, the I-GSSL algorithm is proposed to handle the existing out-of-sample problem where an elastic regularization is considered as an extra constraint. Meanwhile, due to unequal amount of location information in the received signal strength (RSS) from different access points (APs) and the redundancy of RSS at APs, a double weighted K nearest neighbor (DWKNN) algorithm is proposed for localization. Experimental results show the proposed scheme achieves a better label propagation and localization accuracy.