Maurizio Rea

Every year millions of lives are lost in emergency situations. Localizing missing people in the shortest possible timeframe is the most effective tool to reduce such a death toll. However, this is challenging when the victims are unable to communicate by themselves or located in large and difficult-to-reach areas. Current technological approaches for victim localization are often rendered inoperable under obstacles or low visibility conditions, or due to the lack of cellular networking infrastructure. Toward addressing these issues, we present 3DSAR+, a pioneering single-drone 3D cellular SAR solution leveraging 5G-NR technology. 3DSAR+ system introduces dynamic autonomous 3D UAV trajectories in diverse and challenging environments, offering a robust tool for first responders in SAR missions. The main novelty of the proposed approach lies in advanced distance and angle estimation combined with ML algorithms for position prediction and correction. The approach is able to estimate victims’ locations through their mobile phones without requiring extra equipment and improves localization accuracy by an order of magnitude compared to baseline solutions.

With the proliferation of connected devices and the increasing demand for more precise and reliable positioning information, the fusion of 5 G and GNSS localization systems holds immense potential to overcome the current limitations of standalone approaches. In order to tackle this challenge, in this paper we explore the potential of the fusion of 5G-NR and satellite localization systems to jointly enhance their accuracy and reliability. The designed solution, termed 5GNSS , is explained in detail, describing its five main building blocks and corresponding mathematical foundations. An extensive performance evaluation has been conducted, considering urban, suburban, and rural vehicular scenarios, as well as a varying number of visible satellites and 5 G base stations. Our results show that 5GNSS can outperform standalone GNSS, 4G-LTE and 5G-NR positioning systems consistently across all these scenarios with an average error reduction in the $\sim$ 60-40% range. Based on these results, we conclude that future positioning systems would strongly benefit from incorporating 5GNSS-like fusion solutions.

Indoor positioning is a major challenge for location-based services. WiFi deployments are often used to address indoor positioning. Yet, they require multiple access points , which may not be available or accessible for localization in all scenarios, or they make unrealistic assumptions for practical deployments. In this paper we present SPRING+ , a positioning system that extracts and processes Channel State Information (CSI) and Fine Time Measurements (FTM) from a single Access Point (AP) to localize commercial smartphones. First, we propose an adaptive method for estimating the Angle of Arrival (AOA) from CSI that works on single packets and leverages information from the estimated number of paths. Second, we present a new method to detect the first path using FTM measurements, robust to multipath scenarios. We evaluate SPRING+ in an extensive experimental campaign consisting of four different testbeds: i) generic indoor spaces, ii) generic indoor spaces with obstacles, iii) office environments and iv) home environments. Our results show that SPRING+ is able to achieve a median 2D positioning error between 1 and 1.8 meters with a single WiFi AP .

The advent of the fourth Industrial Revolution (Industry 4.0) requires wireless networked solutions to connect machines. However, the industrial environment is notorious for being averse to wireless communication, with traditional wireless resource mechanisms prone to errors because of metallic objects. In this work, we propose to exploit the knowledge of location to derive context information and dynamically allocate wireless resources in time and space to target devices. We exploit the spatial geometry of the Access Points (APs) and we introduce a statistical model that maps the user position’s spatial distribution to an angle error distribution and derive a hypothesis test to declare if the link is under metallic blockage or not. In order to avoid changes to the client side and operate with a single interface radio, we use the same wireless network both for positioning and scheduling. We experimentally show that our system can localize four mobile robots deployed in a very harsh environment with metal obstacles and reflections. Context information applied to wireless resources protocol help increasing up to 40 percent of the network throughput in the above industrial-like scenario.