Paul Meissner

High-accuracy localization for assisted living: 5G systems will turn multipath channels from foe to friend

Assisted living (AL) technologies, enabled by technical advances such as the advent of the Internet of Things, are increasingly gaining importance in our aging society. This article discusses the potential of future high-accuracy localization systems as a key component of AL applications. Accurate location information can be tremendously useful to realize, e.g., behavioral monitoring, fall detection, and real-time assistance. Such services are expected to provide older adults and people with disabilities with more independence and thus to reduce the cost of caretaking. Total cost of ownership and ease of installation are paramount to make sensor systems for AL viable. In case of a radio-based indoor localization system, this implies that a conventional solution is unlikely to gain widespread adoption because of its requirement to install multiple fixed nodes (anchors) in each room. This article therefore places its focus on 1) discussing radiolocalization methods that reduce the required infrastructure by exploiting information from reflected multipath components (MPCs) and 2) showing that knowledge about the propagation environment enables localization with high accuracy and robustness. It is demonstrated that new millimeter-wave (mm-wave) technology, under investigation for 5G communications systems, will be able to provide centimeter (cm)-accuracy indoor localization in a robust manner, ideally suited for AL.

UWB for Robust Indoor Tracking: Weighting of Multipath Components for Efficient Estimation

In a radio propagation channel, deterministic reflections carry important position-related information. With the help of prior knowledge such as a floor plan, this information can be exploited for indoor localization. This letter presents the improvement of a multipath-assisted tracking approach using information about the relevance of deterministic multipath components in an environment. This information is fed to a tracking filter as an observation noise model. It is estimated from a few training signals between anchors and an agent at known positions. Tracking results are presented for measurements in a partial non-line-of-sight environment. At a bandwidth of 2 GHz, an accuracy of 4 cm can be achieved for over 90% of the positions if additional channel information is available. Otherwise, this accuracy is only possible for about 45% of the positions. The covariance of the estimation matches closely to the corresponding Cramèr-Rao lower bound.

Evaluation of Position-Related Information in Multipath Components for Indoor Positioning

Location awareness is a key factor for a wealth of wireless indoor applications. Its provision requires the careful fusion of diverse information sources. For agents that use radio signals for localization, this information may either come from signal transmissions with respect to fixed anchors, from cooperative transmissions between agents, or from radar-like monostatic transmissions. Using a priori knowledge of a floor plan of the environment, specular multipath components can be exploited, based on a geometric-stochastic channel model. In this paper, a unified framework is presented for the quantification of this type of position-related information, using the concept of equivalent Fisher information. We derive analytical results for the Cramér-Rao lower bound of multipath-assisted positioning, considering bistatic transmissions between agents and fixed anchors, monostatic transmissions from agents, cooperative measurements between agents, and combinations thereof, including the effect of clock offsets. Awareness of this information enables highly accurate and robust indoor positioning. Computational results show the applicability of the framework for the characterization of the localization capabilities of a given environment, quantifying the influence of different system setups, signal parameters, and the impact of path overlap.

Performance bounds for multipath-assisted indoor navigation and tracking (MINT)

The MINT (multipath-assisted indoor navigation and tracking) problem exploits the geometry of deterministic multipath components (MPCs) for robust indoor positioning in line-of-sight (LOS) and non-LOS situations. It assumes a known room layout and can thus easily make use of signals reflected by the walls, for instance. In this paper, the Cramer-Rao lower bound of the positioning error is derived for this problem. This requires a novel channel model, where diffuse multipath is modeled as a colored Gaussian process that influences the effective SNR of deterministic MPCs. The adverse effect of path overlap is demonstrated and discussed. Computational results show the three-fold importance of a large signal bandwidth. The bandwidth reciprocal (i.e. the pulse duration) multiplies the error standard deviation - a fundamental result well-known from AWGN channels. But it also multiplies the effective power of the interfering diffuse multipath and gives rise to additional path overlap. A minimum bandwidth of 1 GHz seems appropriate and sufficient.

UWB positioning with virtual anchors and floor plan information

This paper describes an indoor positioning method using ultra-wideband (UWB) signals from one single transmitter at a known location together with a-priori floor plan information. The position of the receiver is estimated from the signal reflections in the room walls and the direct signal path. Therefore, the algorithm is inherently robust to non-line-of-sight conditions, which are considered a key problem in range-based positioning. We introduce likelihood models for the individual range measurements that correspond to single and double reflections. Using the room geometry, these reflections can be mapped to virtual anchors at known positions and the multilateration problem can be solved using statistical techniques. Simulation results show that this approach of position estimation is a promising candidate for robust and accurate localization.

UWB sequential Monte Carlo positioning using virtual anchors

We present a novel UWB indoor localization concept that performs the position estimation with a set of virtual anchor nodes, generated from a single physical anchor and floor plan information. Using range estimates to the virtual anchors, we perform multilateration to estimate the position of an agent. Previous work has shown the general applicability of this concept. In this contribution, we use a moving agent to exploit the correlation in successive positions using state-space concepts. A motion model for the agent and the measurement likelihood function allow for the use of the powerful framework of Bayesian state estimation. With this concept, we can propagate prior information on the agent position from one time step to the next. The statistical model for the ranging to the virtual anchors accounts for several imperfections, which lead to multimodal and heavy-tailed measurement distributions. We show how modified versions of the Kalman filter as well as a particle filter can account for these imperfections and yield accurate and robust position estimates. In a typical indoor pedestrian motion scenario, we can achieve an accuracy of about 45 cm for 90% of the estimates.

Multipath-assisted maximum-likelihood indoor positioning using UWB signals

Multipath-assisted indoor positioning (using ultrawideband signals) exploits the geometric information contained in deterministic multipath components. With the help of a-priori available floorplan information, robust localization can be achieved, even in absence of a line-of-sight connection between anchor and agent. In a recent work, the Cramér-Rao lower bound has been derived for the position estimation variance using a channel model which explicitly takes into account diffuse multipath as a stochastic noise process in addition to the deterministic multipath components. In this paper, we adapt this model for position estimation via a measurement likelihood function and evaluate the performance for real channel measurements. Performance results confirm the applicability of this approach. A position accuracy better than 2.5 cm has been obtained in 90% of the estimates using only one active anchor at a bandwidth of 2 GHz and robustness against non-line-of-sight situations has been demonstrated.

Analysis of an indoor UWB channel for multipath-aided localization

We present a detailed analysis of an indoor UWB channel measurement campaign. The focus is on the modeling of the deterministic part of the multipath channel using a-priori known relevant reflections and scatterers, found from an available floor plan. Our approach uses virtual signal sources, whose locations and visibilities can be calculated using simple ray-launching techniques. The channel analysis steps exploit these results, using an effective multipath cancellation method that introduces virtually no artifacts. We show that the corresponding multipath-components can explain up to 90% of the UWB channel impulse responses in terms of energy capture. This is important for multipath-aided indoor localization, which provides robust position fixes using a single base station only.

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.

Cooperative multipath-assisted indoor navigation and tracking (Co-MINT) using UWB signals

In multipath-assisted indoor navigation and tracking (MINT), explicit use is made of multipath propagation in the ultra-wideband channel. With the help of floorplan information, localization is possible with only one reference node. In this work, we introduce MINT among cooperating users in order to omit the need of any known reference nodes. The received signal is modeled as a combination of deterministic multipath components, diffuse multipath represented by a random process and AWGN. In a mixed line-of-sight (LOS)/non-LOS (NLOS) indoor scenario, we show how information from mono-static and bi-static measurements of cooperating users can be merged for localization and tracking. The problem is formulated with a factor graph and solved via belief propagation on the factor graph. Simulation results show that localization and tracking of a mobile agent is possible: (i) independent of LOS or NLOS, (ii) without the need for further infrastructure.