Graph‐based iterative measurement‐denoising and radio‐map generation for semi‐supervised indoor localisation

Abstract

This study considers the problem of indoor localisation in which the authors try to generate the radio map of the environment based on both labelled and unlabelled measurements (i.e. measurements with and without information about the location that they have been collected). In such problems, the method that has been used for radio-map generation as well as the quality of the measurements (the received signal strength from different environment's WiFi Access Points) are very critical in the overall accuracy of the generated radio map. In this study, they apply two of the well-known graph-based signal processing schemes (namely iterative least square reconstruction and graph-based label propagation) for polishing the input data, determining the outliers and radio-map generation. Experimental results show the superior performance of the proposed method compared to previous schemes.