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GIORGIO GUERZONI
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Pubblicazioni
2023
- Novel Movement-Based Methods for the Calibration of Colocated Multiple-Input Multiple-Output Radars
[Articolo su rivista]
Guerzoni, G.; Faghand, E.; Vitetta, G. M.; Vincenzi, L.; Mehrshahi, E.
abstract
2023
- Radar-Based Monitoring of Vital Signs: A Tutorial Overview
[Articolo su rivista]
Paterniani, G; Sgreccia, D; Davoli, A; Guerzoni, G; Di Viesti, P; Valenti, Ac; Vitolo, M; Vitetta, Gm; Boriani, G
abstract
In the last years, substantial attention has been paid to the use of radar systems in health monitoring, due to the availability of both low-cost radar devices and computationally efficient algorithms for processing their measurements. In this article, a tutorial overview of radar-based monitoring of vital signs is provided. More specifically, we first focus on the available radar technologies and the signal processing algorithms developed for the estimation of vital signs. Then, we provide some useful guidelines that should be followed in the selection of radar devices for vital sign monitoring and in their use. Finally, we illustrate various specific applications of radar systems to health monitoring and some relevant research trends in this field.
2023
- Recursive Algorithms for the Estimation of Multiple Superimposed Undamped Tones and Their Application to Radar Systems
[Articolo su rivista]
Viesti, Pasquale Di; Davoli, Alessandro; Guerzoni, Giorgio; Vitetta, Giorgio M.
abstract
In this article, two recursive algorithms for the detection of multiple superimposed tones in noise and the estimation of their parameters are derived. They are based on a maximum likelihood approach and combine an innovative single-tone estimator with a serial cancellation procedure. Our numerical results lead to the conclusion that the developed methods can achieve a substantially better accuracy–complexity tradeoff than various related techniques in the presence of multiple closely spaced tones. Moreover, they can be exploited to detect multiple closely spaced targets and estimate their spatial coordinates in multiple-input multiple-output frequency-modulated continuous wave radar systems.
2022
- Novel Deterministic Detection and Estimation Algorithms for Colocated Multiple-Input Multiple-Output Radars
[Articolo su rivista]
Di Viesti, Pasquale; Davoli, Alessandro; Guerzoni, Giorgio; Vitetta, Giorgio M.
abstract
In this manuscript, the problem of detecting multiple targets and estimating their spatial coordinates (namely, their range and the direction of arrival of their electromagnetic echoes) in a colocated multiple-input multiple-output radar system operating in a static or slowly changing two-dimensional or three-dimensional propagation scenario is investigated. Various solutions, collectively called range & angle serial cancellation algorithms , are developed for both frequency modulated continuous wave radars and stepped frequency continuous wave radars. Moreover, specific technical problems experienced in their implementation are discussed. Finally, the accuracy achieved by these algorithms in the presence of multiple targets is assessed on the basis of both synthetically generated data and of the measurements acquired through three different multiple-input multiple-output radars and is compared with that provided by other methods based on multidimensional Fourier analysis and multiple signal classification.
2021
- Machine Learning and Deep Learning Techniques for Colocated MIMO Radars: A Tutorial Overview
[Articolo su rivista]
Davoli, A.; Guerzoni, G.; Vitetta, G. M.
abstract
Radars are expected to become the main sensors in various civilian applications, ranging from health-care monitoring to autonomous driving. Their success is mainly due to the availability of both low cost integrated devices, equipped with compact antenna arrays, and computationally efficient signal processing techniques. An increasingly important role in the field of radar signal processing is played by machine learning and deep learning techniques. Their use has been first taken into consideration in human gesture and motion recognition, and in various healthcare applications. More recently, their exploitation in object detection and localization has been also investigated. The research work accomplished in these areas has raised various technical problems that need to be carefully addressed before adopting the above mentioned techniques in real world radar systems. In this manuscript, a comprehensive overview of the machine learning and deep learning techniques currently being considered for their use in radar systems is provided. Moreover, some relevant open problems and current trends in this research area are analysed. Finally, various numerical results, based on both synthetically generated and experimental datasets, and referring to two different applications are illustrated. These allow readers to assess the efficacy of specific methods and to compare them in terms of accuracy and computational effort.
2019
- AUTONOMOUS DRIVING SYSTEM THROUGH ROWS OF A PLANTATION
[Brevetto]
Davoli, Alessandro; DI CECILIA, Luca; DI VIESTI, Pasquale; Ferrari, Luca; Guerzoni, Giorgio; Sirignano, Emilio; Vitetta, Giorgio Matteo
abstract