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PASQUALE DI VIESTI

Ricercatore t.d. art. 24 c. 3 lett. A
Dipartimento di Ingegneria "Enzo Ferrari"
Docente a contratto
Dipartimento di Ingegneria "Enzo Ferrari"

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Pubblicazioni

2023 - An Approximate Maximum Likelihood Method for the Joint Estimation of Range and Doppler of Multiple Targets in OFDM-Based Radar Systems [Articolo su rivista]
Mirabella, M.; Di Viesti, P.; Davoli, A.; Vitetta, G. M.
abstract

In this manuscript, an innovative method for the detection and the estimation of multiple targets in a radar system employing orthogonal frequency division multiplexing is illustrated. The core of this method is represented by a novel algorithm for detecting multiple superimposed two-dimensional complex tones in the presence of noise and estimating their parameters. This algorithm is based on a maximum likelihood approach and combines a single tone estimator with a serial cancellation procedure. Our numerical results lead to the conclusion that the developed method can achieve a substantially better accuracy-complexity trade-off than various related techniques in the presence of closely spaced targets.

2023 - Deterministic Algorithms for Four-Dimensional Imaging in Colocated MIMO OFDM-Based Radar Systems [Articolo su rivista]
Mirabella, M.; Di Viesti, P.; Vitetta, G. M.
abstract

In this manuscript, the problem of detecting multiple targets and jointly estimating their spatial coordinates (namely, the range, the Doppler and the direction of arrival of their electromagnetic echoes) in a colocated multiple-input multiple-output radar system employing orthogonal frequency division multiplexing is investigated. It is well known its optimal solution, namely the joint maximum likelihood estimator of an unknown number of targets, is unfeasible because of its huge computational complexity. Moreover, until now, sub-optimal solutions have not been proposed in the technical literature. In this manuscript a novel approach to the development of reduced complexity solutions is illustrated. It is based on the idea of separating angle estimation from range-Doppler estimation, and of exploiting known algorithms for solving these two sub-problems. A detailed analysis of the accuracy and complexity of various detection and estimation methods based on this approach is provided. Our numerical results evidence that one of these methods is able to approach optimal performance in the maximum likelihood sense with a limited computational effort in different scenarios.

2023 - Deterministic Signal Processing Techniques for OFDM-Based Radar Sensing: An Overview [Articolo su rivista]
Mirabella, M.; Di Viesti, P.; Davoli, A.; Vitetta, G. M.
abstract

In this manuscript, we analyze the most relevant classes of deterministic signal processing methods currently available for the detection and the estimation of multiple targets in a joint communication and sensing system employing orthogonal frequency division multiplexing. Our objective is offering a fair comparison of the available technical options in terms of required computational complexity and accuracy in both range and Doppler estimation. Our numerical results, obtained in various scenarios, evidence that distinct algorithms can achieve a substantially different accuracy-complexity trade-off.

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.

2020 - Multiple Bayesian Filtering as Message Passing [Articolo su rivista]
Vitetta, Giorgio M.; DI VIESTI, Pasquale; Sirignano, Emilio; Montorsi, Francesco
abstract

In this manuscript, a general method for deriving filtering algorithms that involve a network of interconnected Bayesian filters is proposed. This method is based on the idea that the processing accomplished inside each of the Bayesian filters and the interactions between them can be represented as message passing algorithms over a proper graphical model. The usefulness of our method is exemplified by developing new filtering techniques, based on the interconnection of a particle filter and an extended Kalman filter, for conditionally linear Gaussian systems. Numerical results for two specific dynamic systems evidence that the devised algorithms can achieve a better complexity-accuracy tradeoff than marginalized particle filtering and multiple particle filtering.

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

2019 - Double Bayesian Smoothing as Message Passing [Articolo su rivista]
Di Viesti, Pasquale; Vitetta, Giorgio Matteo; Sirignano, Emilio
abstract

Recently, a novel method for developing filtering algorithms, based on the interconnection of two Bayesian filters and called double Bayesian filtering, has been proposed. In this manuscript we show that the same conceptual approach can be exploited to devise a new smoothing method, called double Bayesian smoothing. A double Bayesian smoother combines a double Bayesian filter, employed in its forward pass, with the interconnection of two backward information filters used in its backward pass. As a specific application of our general method, a detailed derivation of double Bayesian smoothing algorithms for conditionally linear Gaussian systems is illustrated. Numerical results for two specific dynamic systems evidence that these algorithms can achieve a better complexity-accuracy tradeoff and tracking capability than other smoothing techniques recently appeared in the literature.

2019 - Marginalized Particle Filtering and Related Filtering Techniques as Message Passing [Articolo su rivista]
Vitetta, Giorgio M.; Sirignano, Emilio; DI VIESTI, Pasquale; Montorsi, Francesco; Sola, Matteo
abstract

In this paper, a factor graph approach is employed to investigate the recursive filtering problem for conditionally linear Gaussian state-space models. First, we derive a new factor graph for the considered filtering problem; then, we show that applying the sum-product rule to our graphical model results in both known and novel filtering techniques. In particular, we prove that: 1) marginalized particle filtering can be interpreted as a form of forward only message passing over the devised graph; 2) novel filtering methods can be easily developed by exploiting the graph structure and/or simplifying probabilistic messages.