No abstract

This paper presents and extends the idea of multipath assisted positioning, named Channel-SLAM. Generally, multipath reception degrades the accuracy of the positioning device as long as the receiver is based on standard methods. In contrast, Channel-SLAM uses the multipath propagation of the wireless signal to allow positioning in cases of insufficient number of transmitters or increase the accuracy otherwise. Channel-SLAM treats multipath components (MPCs) as signals from virtual transmitters (VTs) which are time synchronized to the physical transmitter and fixed in their position. To use the information of the MPCs, Channel-SLAM estimates the receiver position and the position of the VTs simultaneously and does not require any prior information such as room-layout or a database for fingerprinting. The simultaneous localization and mapping (SLAM) algorithm is used by the receiver to estimate its own position and the position of VTs as landmarks. This paper investigates mapping of the receiver position, where we derive a probabilistic map representation based on locations. Thus, if the receiver knows its current location, we also know the probability where the receiver moves for the next step. In order to estimate and store the probability distribution of receivers motions as a function of location, we propose a probabilistic map that represents the receiver motion in a two-dimensional hexagonal grid. Hence, as soon as the receiver returns to an already mapped position, information of this position can be reused for positioning to obtain better position estimations of the receiver position. The algorithm is evaluated based on measurements with one fixed transmitter and a moving pedestrian which moves on partially overlapping loops. Based on these evaluations, we show, that the algorithm is able to accurately map the trajectory as well as reuse estimated map.

No abstract

No abstract

No abstract