Erik Leitinger

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.

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.

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.

On the use of ray tracing for performance prediction of UWB indoor localization systems

The most important factors impairing the performance of radio-based indoor localization systems are propagation effects like strong reflections or diffuse scattering. To the full extent, these effects can be captured only by time-consuming measurement campaigns. Ray tracing (RT) offers the possibility to predict the radio channel for a certain environment, avoiding the need for measurements. However, it is crucial to include all relevant propagation mechanisms in the RT as well as to validate the obtained results. In this paper, we analyze if sub-band divided RT can yield realistic ultra-wideband channel impulse responses. We use the RT results for performance analysis of multipath-assisted localization, which depends directly upon the above mentioned propagation effects. In particular, it has been shown that the ratio of the signal energies of deterministically reflected paths to interfering diffuse components quantifies the amount of position-related information of deterministic multipath components. Comparison of this ratio to measurement data is thus useful to validate the sub-band divided RT. The results highlight the need for proper modeling of the diffuse multipath, as estimates of this energy ratio using RT are often overly optimistic. However, the obtained localization performance predictions using measurements and RT show general agreement.

Bandwidth Scaling and Diversity Gain for Ranging and Positioning in Dense Multipath Channels

The Cramér-Rao lower bound on the ranging error variance is revisited to quantify the influence of dense multipath in indoor environments. Our analytical results yield novel insight on the scaling of the ranging and positioning accuracy as a function of bandwidth and number of diversity branches. It also yields insight on the detectability of the useful line-of-sight signal component. It is found that the Fisher information scales faster than quadratically in bandwidth but only linearly in the number of independent diversity branches. We investigate the entire bandwidth-range from the flat-fading narrowband case up to ultra-wideband.

Cognitive radar for the localization of RFID transponders in dense multipath environments

High-accuracy localization remains a much desired but elusive feature for passive radio transponders as used in radio-frequency identification (RFID). We believe that the principle of cognitive radar can overcome the fundamental physical limitations hindering its implementation. We propose to jointly employ a narrowband radio to interrogate the transponders and an adaptive (ultra) wideband backscatter radio for the target tracking and for actuating, sensing, and learning the radio environment. This paper explores system model and key processing perception-action cycle steps of such a cognitive secondary radar. At its core is a perception-action cycle, which consists of transmitter and receiver-side environment models for representing radio channel conditions and Bayesian trackers for the target states. Multipath is exploited to improve the robustness and to make optimum use of the radars sensing capabilities. Feedback information is derived from the Cramέr-Rao lower bound on the position error. Initial results are presented as a basic proof of principle.

MIMO gain and bandwidth scaling for RFID positioning in dense multipath channels

This paper analyzes the achievable ranging and positioning performance for two design constraints in a radio frequency identification (RFID) system: (i) the bandwidth of the transmit signal and (ii) the use of multiple antennas at the readers. The ranging performance is developed for correlated and uncorrelated constituent channels by utilizing a geometry-based stochastic channel model for the downlink and the uplink. The ranging error bound is utilized to compute the precision gain for a ranging scenario with multiple collocated transmit and receive antennas. The position error bound is then split into a monostatic and bistatic component to analyze the positioning performance in a multiple input, multiple output (MIMO) RFID system. Simulation results indicate that the ranging variance is approximately halved when utilizing uncorrelated constituent channels in a monostatic setup. It is shown that both the bandwidth and the number of antennas decrease the error variance roughly quadratically.