A Semi-Supervised WLAN Indoor Localization Method Based on 1-Graph Algorithm

For indoor location estimation based on received signal strength (RSS) in wireless local area networks (WLAN), in order to reduce the influence of noise on the positioning accuracy, a large number of RSS should be collected in offline phase. Therefore, collecting training data with positioning information is time consuming which becomes the bottleneck of WLAN indoor localization. In this paper, the traditional semi-supervised learning method based on k-NN and ε-NN graph for reducing collection workload of offline phase are analyzed, and the result shows that the k-NN or ε-NN graph are sensitive to data noise, which limit the performance of semi-supervised learning WLAN indoor localization system. Aiming at the above problem, it proposes a 1-graph-algorithm-based semi-supervised learning (LG-SSL) indoor localization method in which the graph is built by 1-norm algorithm. In our system, it firstly labels the unlabeled data using LG-SSL and labeled data to build the Radio Map in offline training phase, and then uses LG-SSL to estimate user’s location in online phase. Extensive experimental results show that, benefit from the robustness to noise and sparsity of 1-graph, LG-SSL exhibits superior performance by effectively reducing the collection workload in offline phase and improving localization accuracy in online phase.