Enhancing smartphone indoor localization via opportunistic sensing

Abstract

Using a mobile phone for fine-grained indoor localization remains an open problem. Low-complexity approaches without infrastructure could not achieve accurate and reliable results due to various restrictions. Accurate solutions relying on dense anchor nodes are inconvenient and cumbersome in deployment. The anchor blockage problem would further reduce the effective coverages. In this paper, we investigate the problems associated with improving indoor localization of a mobile phone via opportunistic anchor sensing, a new sensing paradigm leveraging multiple anchors without minimum number or constellation requirement. One key motivation is that the location results could be improved by exploring more data types rather than deploying more anchor nodes. To enable this high scalability and accuracy design, we leverage low-coupling hybrid ranging by our low cost anchor nodes with centimeter-level relative distance estimation. Activity pattern extracted in local smartphone is utilized for accurate displacement and direction estimation. Finer localization resolution could be achieved with sufficient anchor access. We introduce the delay-constraint robust semidefinite programming in trilateration calculation with the potential of centimeter-level location resolution. We conduct extensive experiments in various scenarios. Compared with other approaches, opportunistic sensing could improve the location accuracy, scalability as well as robustness under various anchor accessibilities.