Simultaneous localization and mapping using multipath channel information

Location awareness is one of the most important requirements for many future wireless applications. Multipath-assisted indoor navigation and tracking (MINT) is a possible concept to enable robust and accurate localization of an agent in indoor environments. Using a-priori knowledge of a floor plan of the environment and the position of the physical anchors, specular multipath components can be exploited, based on a geometric-stochastic channel model. So-called virtual anchors (VAs), which are mirror images of the physical anchors, are used as additional anchors for positioning. The quality of this additional information depends on the accuracy of the corresponding floor plan. In this paper, we propose a new simultaneous localization and mapping (SLAM) approach that allows to learn the floor plan representation and to deal with inaccurate information. A key feature is an online estimated channel characterization that enables an efficient combination of the measurements. Starting with just the known anchor positions, the proposed method includes the VA positions also in the state space and is thus able to adapt the VA positions during tracking of the agent. Furthermore, the method is able to discover new potential VAs in a feature-based manner. This paper presents a proof of concept using measurement data. The excellent agent tracking performance—90%of the error lower than 5 cm—achieved with a known floor plan can be reproduced with SLAM.