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PASQUALE DI VIESTI

Assegnista di ricerca
Dipartimento di Ingegneria "Enzo Ferrari"
Docente a contratto
Dipartimento di Ingegneria "Enzo Ferrari"


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Pubblicazioni

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.