Initialization and Online-Learning of RSS Maps for Indoor / Campus Localization

Abstract

Common approaches for indoor positioning based on cellular communication systems use as measurements the received signal strength (RSS). In order to work properly, such a system often requires many calibration points before its start. This paper presents a two-fold approach achieving high indoor localization accuracies without requiring too many calibration points. The basic idea is to use an initial propagation model with few parameters, which can be adapted by a few measurements, e.g. mutual measurements of access points. Then the model is refined by incorporating additional parameters and using online learning. Investigations on the requirements and potentials of different approaches and results for DECT and WLAN setups are given. The first approach uses predefined paths that should be passed through by a service technician with measurement equipment. The second approach uses a Kohonen-like learning algorithm to adapt the model on-the-fly. For both approaches linear propagation models and more involved dominant path models incorporating map information are applied for the initialization.