The advent of the fourth industrial revolution (Industry 4.0) aims at increasing automation and efficiency in manufacturing processes by the adoption of information and communication technologies. Several of the proposed solutions rely on precise localization of material, equipment, or operators. This article investigates the employment of ultrawideband (UWB) real-time location systems (RTLS) in a factory environment and proposes an augmentation technique to mitigate the impairments that arise in such a complex scenario. A Bayesian filtering method is developed to jointly track the motion dynamics and the time-varying visibility conditions of the UWB antennas, with particle-based implementation to deal with the nonlinearity of the UWB measurements. Laboratory tests and industrial experiments are carried out to evaluate the performance of three commercial off-the-shelf UWB technologies: Decawave, Sewio, and Ubisense. The experimental data are then used to calibrate and test the developed filtering technique, showing that it is possible to significantly reduce the positioning error originating from dense multipath and NLOS effects by jointly tracking the target dynamics and visibility conditions.

The widespread adoption of the fifth generation (5G) of cellular networks has brought new opportunities for the development of localization-based services. High-accuracy positioning use cases and functionalities defined by the standards are drawing the interest of vertical industries. In the transition towards the deployment, this paper aims to provide an in-depth tutorial on 5G positioning, summarizing the evolutionary path that led to the standardization of cellular-based positioning, describing the localization elements in current and forthcoming releases of the Third Generation Partnership Project (3GPP) standard, and the major research trends. By providing fundamental notions on wireless localization, comprehensive definitions of measurements and architectures, examples of algorithms, and details on simulation approaches, this paper is intended to represent an exhaustive guide for researchers and practitioners. Our approach aims to merge practical aspects of enabled use cases and related requirements with theoretical methodologies and fundamental bounds, allowing to understand the trade-off between system complexity and achievable, i.e., tangible, benefits of 5G positioning services. We analyze the performance of 3GPP Rel-16 positioning by standard-compliant simulations in realistic outdoor and indoor propagation environments, investigating the impact of the system configuration and the limitations to be resolved for delivering accurate positioning solutions.

Precise vehicle positioning is a key element for the development of Cooperative Intelligent Transport Systems (C-ITS). In this context, we present a distributed processing technique to augment the performance of conventional Global Navigation Satellite Systems (GNSS) exploiting Vehicle-to-anything (V2X) communication systems. We propose a method, referred to as Implicit Cooperative Positioning with Data Association (ICP-DA), where the connected vehicles detect a set of passive features in the driving environment, solve the association task by pairing them with on-board sensor measurements and cooperatively localize the features to enhance the GNSS accuracy. We adopt a belief propagation algorithm to distribute the processing over the network, and solve both the data association and localization problems locally at vehicles. Numerical results on realistic traffic networks show that the ICP-DA method is able to significantly outperform the conventional GNSS. In particular, the analysis on a real urban road infrastructure highlights the robustness of the proposed method in real-life cases where the interactions among vehicles evolve over space and time according to traffic regulation mechanisms. Performances are investigated both in conventional traffic-light regulated scenarios and self-regulated environments (as representative of future automated driving scenarios) where vehicles autonomously cross the intersections taking gap-availability decisions for avoiding collisions. The analysis shows how the mutual coordination in platoons of vehicles eases the cooperation process and increases the positioning performance.

Radio environment map (REM) reconstruction based on large-scale channel measurements is a promising technology for future mobility services involving vehicle-to-everything (V2X) communications. REMs provide contextual information which can be exploited to reduce V2X communication latency and control signaling, for instance, through a fast access to channel state information. However, the accuracy of radio mapping techniques is limited by the availability of measurements, which require for collection significant signaling overhead. Moreover, mobility scenarios impose strict latency constraints that render fast channel acquisition a challenging problem. This paper presents a low-complexity deep-learning-based approach based on long-short term memory (LSTM) cells for REM reconstruction on roads, addressed as a data-filling problem. To improve model generalization, the network is trained on a virtually infinite dataset generated according to a 3GPP-compliant freeway scenario, considering different correlation properties and missing point configurations. The results show that the proposed approach provides a performance closer to the theoretical lower bound than the classical Ordinary Kriging spatial interpolation method, without increasing the complexity order. Experiments performed in realistic scenarios using a 3D city model confirm the generalization capability of the proposed solution.