Sensor radar networks (SRNs) employing ultra-wideband (UWB) signals are a prominent solution for accurate localization and tracking in indoor environments. However, tracking device-free targets via SRNs is challenging, especially in environments heavily affected by clutter. Clutter characterization is vital to derive performance benchmarks as well as to design inference algorithms for SRNs. Examples of clutter statistical characterization have been provided in the literature for conventional SRNs employing narrowband signals in outdoor scenarios. However, considerably less effort has been devoted for SRNs employing UWB signals in indoor environments. This paper proposes an approach to characterize the clutter-plus-noise component after mitigation filtering in UWB SRNs. In particular, the statistical properties of the residual clutter-plus-noise are derived by applying statistical tests on measurements gathered in an indoor environment via UWB sensor radar networks.

Location information and context-awareness are essential for a variety of existing and emerging 5G-based applications. Nevertheless, navigation satellite systems are denied in in-door environments, current cellular systems fail to provide high-accuracy localization, and other local localization technologies (e.g., Wi-Fi or Bluetooth) imply high deployment, maintenance and integration costs. Raw spatiotemporal data are not sufficient by themselves and need to be integrated with tools for the analysis of the behavior of physical targets, to extract relevant features of interests. In this paper, we present LOCUS, an H2020 project (https://www.locus-project.eu/) funded by the European Commission, aiming at the design and implementation of an innovative location management layered platform which will be able to: i) improve localization accuracy, close to theoretical bounds, as well as localization security and privacy, ii) extend localization with physical analytics, iii) extract value out from the combined interaction of localization and analytics, while guaranteeing users' privacy.

Location awareness is vital for emerging Internet-of-Things applications and opens a new era for Localization-of-Things. This paper first reviews the classical localization techniques based on single-value metrics, such as range and angle estimates, and on fixed measurement models, such as Gaussian distributions with mean equal to the true value of the metric. Then, it presents a new localization approach based on soft information (SI) extracted from intra- and inter-node measurements, as well as from contextual data. In particular, efficient techniques for learning and fusing different kinds of SI are described. Case studies are presented for two scenarios in which sensing measurements are based on: 1) noisy features and non-line-of-sight detector outputs and 2) IEEE 802.15.4a standard. The results show that SI-based localization is highly efficient, can significantly outperform classical techniques, and provides robustness to harsh propagation conditions.