Cooperative Multipath Assisted Positioning

Abstract

Location-awareness has become an important issue in modern life. In environments with good view to the sky, global navigation satellite systems can provide very accurate location estimates. Though, their positioning performance decreases significantly if the view to satellites is blocked. Indoors, no localization solution may be obtained at all. Terrestrial signals such as from telecommunication base stations or wireless local area network routers can be used for indoor localization. However, the localization performance may suffer from multipath propagation when traditional propagation delay-based algorithms are applied. The transmit signal is reflected, scattered and diffracted in the environment, distorting the signal and leading to a bias in the delay estimates. With multipath assisted positioning, a new approach has emerged, that exploits the information in multipath components (MPCs) by treating them as line-of-sight signals from virtual transmitters. While the locations of the physical and virtual transmitters are generally unknown, they can be estimated with simultaneous localization and mapping (SLAM). One SLAM based multipath assisted positioning algorithm is Channel-SLAM, where the user location is estimated simultaneously with creating a map of physical and virtual transmitters. The Channel-SLAM algorithm is limited to single users. There are many settings where multiple users move in the same indoor environment, such as in shopping malls or public buildings. Within this thesis, the Channel-SLAM algorithm is extended by cooperation among users in terms of exchanging maps of transmitter locations. This cooperation can drastically increase the positioning performance of all cooperating users. However, Channel-SLAM is only a relative localization system, as no relation of the user location to an absolute coordinate frame is known. The transformation parameters relating the coordinate systems of a user to the coordinate system of a map the user obtains from a different user are unknown. In addition, the correspondences among transmitters observed by the user and transmitters in the map are unknown. Estimating these transformation parameters and correspondences is referred to as map matching. A robust map matching scheme is crucial for such cooperation. Since the lack of diversity among virtual transmitters leads to ambiguities in map matching, the transmitter state space is augmented by information from where transmitters are visible. A transmitter is visible, if its signal can be received in a LoS condition. We derive two new tracking filters for Channel-SLAM that map not only the locations, but also the visibility information on transmitters. The visibility information increases the robustness of Channel-SLAM by facilitating the detection of loop closures. In addition, it is used to derive a robust data association scheme. Data association in multipath assisted positioning refers to the challenge of associating signal components with transmitters, leading to robust and accurate long-term SLAM. Finally, the increased transmitter diversity through visibility information improves the positioning performance of Channel SLAM by a more robust data association and by enabling user cooperation.