Modelling and Experimental Assessment of Inter-Personal Distancing Based on Shared GNSS Observables

Abstract

In the last few years, all countries worldwide have fought the spread of SARS-CoV-2 (COVID-19) by exploiting Information and Communication Technologies (ICT) to perform contact tracing. In parallel, the pandemic has highlighted the relevance of mobility and social distancing among citizens. The monitoring of such aspects appeared prominent for reactive decision-making and the effective tracking of the infection chain. In parallel to the proximity sensing among people, indeed, the concept of social distancing has captured the attention to signal processing algorithms enabling short-to-medium range distance estimation to provide behavioral models in the emergency. By exploiting the availability of smart devices, the synergy between mobile network connectivity and Global Navigation Satellite Systems (GNSS), cooperative ranging approaches allow computing inter-personal distance measurements in outdoor environments through the exchange of light-weight navigation data among interconnected users. In this paper, a model for Inter-Agent Ranging (IAR) is provided and experimentally assessed to offer a naive collaborative distancing technique that leverages these features. Although the technique provides distance information, it does not imply the disclosure of the user’s locations being intrinsically prone to protect sensitive user data. A statistical error model is presented and validated through synthetic simulations and real, on-field experiments to support implementation in GNSS-equipped mobile devices. Accuracy and precision of IAR measurements are compared to other consolidated GNSS-based techniques showing comparable performance at lower complexity and computational effort.

Flexible Metasignal Tracking: Architecture Robustness in Challenging PNT Scenarios

“…Inside the DLL (Fig. 2), the two channels are combined at a post correlation level, exploiting the property in (5) and thus merging the correlators outputs instead of the meta-signal components. In the proposed scheme, a classical early-late (E-L) architecture is used [24].…”

“…IN addition to earth-based navigation, positioning, navigation, and timing (PNT) services are already exploited in a plethora of applications [1]. Among these, precision agriculture [2], vehicle and inter-personal ranging [3], [4], [5], communication networks [6], or space applications [7], [8], [9], to name a few. However, as the range of applications expands, providing an adequate PNT service is becoming more challenging and, nevertheless, an increasing number of current and envisioned applications demands a precise and reliable PNT system [1].…”

DGNSS Cooperative Positioning in Mobile Smart Devices: A Proof of Concept

“…The design and development of the proposed Proof Of Concept (PoC) were inspired by a set of relevant research works discussing the use of differential GNSS measurements in cooperative contexts and the use of GNSS in mobile smart devices and urban mobility in general [27]. More specifically, a number of promising applications have been investigated in the context of vehicular networks and urban network infrastructures [28]- [34].…”

Improving GNSS Spoofing Awareness in Smartphones via Statistical Processing of Raw Measurements

“…These benefits have been demonstrated for code-based, aided, differential and precise point positioning [5]- [7]. Furthermore, collaborative navigation approaches leveraged raw measurements to offer a naive collaborative distancing technique [8] and a GNSS-only enhancement of Position, Velocity, Timing (PVT) estimation accuracy [9]. Although the standalone position computed from raw GNSS data may not be as accurate as the ones obtained through an onboard sensor and network integration (i.e., Google Fused Location Provider), the use of raw GNSS data and adhoc implemented algorithms can improve the solution with respect to unaided, standalone GNSS solutions.…”