arXiv subjects

Marco Canil

Publications and source records attributed to Marco Canil.

5 recordsLinked to original sources

arXiv2026

Estimating Target Doppler in Unsynchronized Multistatic ISAC Deployments with Mobile Nodes

Integrated Sensing And Communication (ISAC) is recognized as a key enabler for future 6th Generation (6G) networks, combining communication capabilities with pervasive sensing. In such systems, the estimation of the Doppler shift plays a crucial role for target characterization. However, typical real-world ISAC scenarios largely involve bistatic or multistatic configurations and mobile ISAC nodes. Under these conditions, Doppler estimation becomes particularly challenging, as clock asynchrony between the Transmitter (TX) and the Receivers (RXs), combined with their mobility, introduces additional Doppler components and phase offsets that distort or disrupt the target-induced frequency shift. Existing works have considered these challenges separately or relied on external reference reflectors. In this paper, we present the first method to estimate the Doppler frequency of a target with mobile and asynchronous ISAC nodes in a multistatic configuration, considering the case of a mobile TX and multiple static RXs, and without leveraging any external reflector. By leveraging the invariance of the phase offsets across multipath components and exploiting geometrical relationships, we show that the problem is solvable if at least 4 RXs are present. We evaluate the proposed solution through numerical simulations in various scenarios, showing that it is a valid approach for estimating target Doppler shifts in unsynchronized multistatic ISAC deployments with mobile nodes.

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arXiv2025

Millimeter-Scale Absolute Carrier Phase-Based Localization in Multi-Band Systems

Localization is a key feature of future Sixth Generation (6G) net-works with foreseen accuracy requirements down to the millimeter level, to enable novel applications in the fields of telesurgery, high-precision manufacturing, and others. Currently, such accuracy requirements are only achievable with specialized or highly resource-demanding systems, rendering them impractical for more wide-spread deployment. In this paper, we present the first system that enables low-complexity and low-bandwidth absolute 3D localization with millimeter-level accuracy in generic wireless networks. It performs a carrier phase-based wireless localization refinement of an initial location estimate based on successive location-likelihood optimization across multiple bands. Unlike previous phase unwrapping methods, our solution is one-shot. We evaluate its performance collecting ~350, 000 measurements, showing an improvement of more than one order of magnitude over classical localization techniques. Finally, we will open-source the low-cost, modular FR3 front-end that we developed for the experimental campaign.

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arXiv2023

An Experimental Prototype for Multistatic Asynchronous ISAC

We prototype and validate a multistatic mmWave ISAC system based on IEEE802.11ay. Compensation of the clock asynchrony between each TX and RX pair is performed using the sole LoS wireless signal propagation. As a result, our system provides concurrent target tracking and micro-Doppler estimation from multiple points of view, paving the way for practical multistatic data fusion. Our results on human movement sensing, complemented with precise, quantitative GT data, demonstrate the enhanced sensing capabilities of multistatic ISAC, due to the spatial diversity of the receiver nodes.

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arXiv2022

ORACLE: Occlusion-Resilient and Self-Calibrating mmWave Radar Network for People Tracking

Millimeter wave (mmWave) radar sensors are emerging as valid alternatives to cameras for the pervasive contactless monitoring of people in indoor spaces. However, commercial mmWave radars feature a limited range (up to $6$-$8$ m) and are subject to occlusion, which may constitute a significant drawback in large, crowded rooms characterized by a challenging multipath environment. Thus, covering large indoor spaces requires multiple radars with known relative position and orientation and algorithms to combine their outputs. In this work, we present ORACLE, an autonomous system that (i) integrates automatic relative position and orientation estimation from multiple radar devices by exploiting the trajectories of people moving freely in the radars' common fields of view, and (ii) fuses the tracking information from multiple radars to obtain a unified tracking among all sensors. Our implementation and experimental evaluation of ORACLE results in median errors of $0.12$ m and $0.03^\circ$ for radars location and orientation estimates, respectively. Fused tracking improves the mean target tracking accuracy by $27\%$, and the mean tracking error is $23$ cm in the most challenging case of $3$ moving targets. Finally, ORACLE does not show significant performance reduction when the fusion rate is reduced to up to 1/5 of the frame rate of the single radar sensors, thus being amenable to a lightweight implementation on a resource-constrained fusion center.

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arXiv2021

MilliTRACE-IR: Contact Tracing and Temperature Screening via mm-Wave and Infrared Sensing

Social distancing and temperature screening have been widely employed to counteract the COVID-19 pandemic, sparking great interest from academia, industry and public administrations worldwide. While most solutions have dealt with these aspects separately, their combination would greatly benefit the continuous monitoring of public spaces and help trigger effective countermeasures. This work presents milliTRACE-IR, a joint mmWave radar and infrared imaging sensing system performing unobtrusive and privacy preserving human body temperature screening and contact tracing in indoor spaces. milliTRACE-IR combines, via a robust sensor fusion approach, mmWave radars and infrared thermal cameras. It achieves fully automated measurement of distancing and body temperature, by jointly tracking the subjects's faces in the thermal camera image plane and the human motion in the radar reference system. Moreover, milliTRACE-IR performs contact tracing: a person with high body temperature is reliably detected by the thermal camera sensor and subsequently traced across a large indoor area in a non-invasive way by the radars. When entering a new room, a subject is re-identified among several other individuals by computing gait-related features from the radar reflections through a deep neural network and using a weighted extreme learning machine as the final re-identification tool. Experimental results, obtained from a real implementation of milliTRACE-IR, demonstrate decimeter-level accuracy in distance/trajectory estimation, inter-personal distance estimation (effective for subjects getting as close as 0.2 m), and accurate temperature monitoring (max. errors of 0.5{\deg}C). Furthermore, milliTRACE-IR provides contact tracing through highly accurate (95%) person re-identification, in less than 20 seconds.

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