Assisted living (AL) technologies, enabled by technical advances such as the advent of the Internet-of-Things, are increasingly gaining importance in our ageing 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 for caretaking.Total cost of ownership and ease of installation are paramount to make sensor systems for AL viable. In case of a radiobased 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 paper therefore places its focus on (i) discussing radiolocalization methods that reduce the required infrastructure by exploiting information from reflected multipath components and (ii) 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 cm-accuracy indoor localization in a robust manner, ideally suited for AL.

We present a simultaneous localization and mapping (SLAM) algorithm that is based on radio signals and the association of specular multipath components (MPCs) with geometric features. Especially in indoor scenarios, robust localization from radio signals is challenging due to diffuse multipath propagation, unknown MPC-feature association, and limited visibility of features. In our approach, specular reflections at flat surfaces are described in terms of virtual anchors (VAs) that are mirror images of the physical anchors (PAs). The positions of these VAs and possibly also of the PAs are unknown. We develop a Bayesian model of the SLAM problem and represent it by a factor graph, which enables the use of belief propagation (BP) for efficient marginalization of the joint posterior distribution. The resulting BP-based SLAM algorithm detects the VAs associated with the PAs and estimates jointly the time-varying position of the mobile agent and the positions of the VAs and possibly also of the PAs, thereby leveraging the MPCs in the radio signal for improved accuracy and robustness of agent localization. The algorithm has a low computational complexity and scales well in all relevant system parameters. Experimental results using both synthetic measurements and real ultra-wideband radio signals demonstrate the excellent performance of the algorithm in challenging indoor environments.

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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 inbetween 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 inbetween 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.

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