User Tracking with Multipath Assisted Positioning-based Fingerprinting and Deep Learning

Multipath assisted positioning schemes allow localizing a user with only a single physical transmitter by treating multipath components (MPCs) as line-of-sight signals from virtual transmitters. The user position and the locations of the physical and virtual transmitters can be estimated jointly with simultaneous localization and mapping (SLAM). While such approaches often show very good positioning performance, they come at the cost of a high computational complexity. To reduce this complexity, multipath assisted positioning schemes based on SLAM may be combined with fingerprinting, where the fingerprints are features of the wireless radio channel. Within this paper, we present such an approach, where a deep neural network (DNN) is trained on data from a multipath assisted positioning scheme to predict the user position and the corresponding uncertainty from channel information. Based on the DNN, a Kalman filter can accurately and efficiently track the user position. We show by simulations that the positioning performance is improved by a factor of 1.5 while the computational complexity is crucially lower than that of multipath assisted positioning-based SLAM

Cooperative Estimation of Maps of Physical and Virtual Radio Transmitters

In multipath assisted positioning, the spatial information contained in multipath components (MPCs) is exploited, as MPCs are regarded as line-of-sight signals from virtual transmitters. The positions of physical and virtual transmitters can be estimated jointly with the receiver position with simultaneous localization and mapping (SLAM). In our multipath assisted positioning approach called Channel-SLAM, the estimates from a channel estimator are used in a Rao-Blackwellized particle filter which implements SLAM. While the original Channel-SLAM algorithm is a single-user positioning system, we present a comprehensive framework for cooperative Channel-SLAM within this paper. Users cooperate by exchanging maps of estimated transmitter locations. With prior information about the locations of physical and virtual transmitters, the positioning performance of the users increases significantly. The more users contribute to such a transmitter map, the more increases the positioning performance. With simulations in an indoor scenario, we show that the positioning performance is bounded for cooperative Channel-SLAM in the long run

Massive MIMO-based Localization and Mapping Exploiting Phase Information of Multipath Components

In this paper, we present a robust multipath-based localization and mapping framework that exploits the phases of specular multipath components (MPCs) using a massive multiple-input multiple-output (MIMO) array at the base station. Utilizing the phase information related to the propagation distances of the MPCs enables the possibility of localization with extraordinary accuracy even with limited bandwidth. The specular MPC parameters along with the parameters of the noise and the dense multipath component (DMC) are tracked using an extended Kalman filter (EKF), which enables to preserve the distance-related phase changes of the MPC complex amplitudes. The DMC comprises all non-resolvable MPCs, which occur due to finite measurement aperture. The estimation of the DMC parameters enhances the estimation quality of the specular MPCs and therefore also the quality of localization and mapping. The estimated MPC propagation distances are subsequently used as input to a distance-based localization and mapping algorithm. This algorithm does not need prior knowledge about the surrounding environment and base station position. The performance is demonstrated with real radio-channel measurements using an antenna array with 128 ports at the base station side and a standard cellular signal bandwidth of 40 MHz. The results show that high accuracy localization is possible even with such a low bandwidth.Comment: 14 pages (two columns), 13 figures. This work has been submitted to the IEEE Transaction on Wireless Communications for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Multipath Assisted Positioning with Transmitter Visibility Information

In multipath assisted positioning, multipath components (MPCs) are regarded as line-of-sight (LoS) signals from virtual transmitters. Instead of trying to mitigate the influence of MPCs, the spatial information contained in MPCs is exploited for localization. The locations of the physical and virtual transmitters are in general unknown but can be estimated with simultaneous localization and mapping (SLAM). Recently, a multipath assisted positioning algorithm named Channel-SLAM for terrestrial radio signals has been introduced. It simultaneously tracks the position of a receiver and maps the locations of physical and virtual radio transmitters. Maps of estimated transmitter locations can be augmented by additional information. Within this paper, we propose to extend the Channel-SLAM algorithm by mapping information about the visibility of transmitters. A physical or virtual transmitter is visible, if its signal is received in a LoS condition. We derive a novel particle filter for Channel-SLAM that estimates and exploits visibility information on transmitters in addition to their locations. We show by means of simulations in an indoor scenario that our novel particle filter improves the positioning performance of Channel-SLAM considerably

Entropy of Transmitter Maps in Cooperative Multipath Assisted Positioning

In multipath assisted positioning, multipath components (MPCs) are regarded as line-of-sight (LoS) signals from virtual transmitters. The locations of physical and virtual transmitters are typically unknown, but can be estimated jointly with the location of a mobile terminal using simultaneous localization and mapping (SLAM). When users cooperate by exchanging maps of estimated positions of physical and virtual transmitters, the positioning performance can be improved drastically. Within this paper, we investigate such transmitter maps that are shared among users. We derive an approximation of the entropy of transmitter maps that is based on the unscented transform and analyze the evolution of this entropy over time. Our simulations indicate that the transmitter maps converge quickly