Zobrazeno 1 - 10
of 265
pro vyhledávání: '"Braca, Paolo"'
Autor:
Marino, Angela, Soldi, Giovanni, Gaglione, Domenico, Aubry, Augusto, Braca, Paolo, De Maio, Antonio, Willett, Peter
Multi-platform radar networks (MPRNs) are an emerging sensing technology due to their ability to provide improved surveillance capabilities over plain monostatic and bistatic systems. The design of advanced detection, localization, and tracking algor
Externí odkaz:
http://arxiv.org/abs/2308.06972
Autor:
Soldi, Giovanni, Gaglione, Domenico, Raponi, Simone, Forti, Nicola, d'Afflisio, Enrica, Kowalski, Paweł, Millefiori, Leonardo M., Zissis, Dimitris, Braca, Paolo, Willett, Peter, Maguer, Alain, Carniel, Sandro, Sembenini, Giovanni, Warner, Catherine
The explosions on September 26th, 2022, which damaged the gas pipelines of Nord Stream 1 and Nord Stream 2, have highlighted the need and urgency of improving the resilience of Underwater Critical Infrastructures (UCIs). Comprising gas pipelines and
Externí odkaz:
http://arxiv.org/abs/2302.01817
We study the performance of machine learning binary classification techniques in terms of error probabilities. The statistical test is based on the Data-Driven Decision Function (D3F), learned in the training phase, i.e., what is thresholded before t
Externí odkaz:
http://arxiv.org/abs/2301.07104
Autor:
Braca, Paolo, Millefiori, Leonardo M., Aubry, Augusto, Marano, Stefano, De Maio, Antonio, Willett, Peter
We study the performance -- and specifically the rate at which the error probability converges to zero -- of Machine Learning (ML) classification techniques. Leveraging the theory of large deviations, we provide the mathematical conditions for a ML c
Externí odkaz:
http://arxiv.org/abs/2207.10939
Recent deep learning methods for vessel trajectory prediction are able to learn complex maritime patterns from historical Automatic Identification System (AIS) data and accurately predict sequences of future vessel positions with a prediction horizon
Externí odkaz:
http://arxiv.org/abs/2205.05404
Autor:
Gaglione, Domenico, Braca, Paolo, Soldi, Giovanni, Meyer, Florian, Hlawatsch, Franz, Win, Moe Z.
Tracking multiple time-varying states based on heterogeneous observations is a key problem in many applications. Here, we develop a statistical model and algorithm for tracking an unknown number of targets based on the probabilistic fusion of observa
Externí odkaz:
http://arxiv.org/abs/2111.13589
Autor:
Brambilla, Mattia, Gaglione, Domenico, Soldi, Giovanni, Mendrzik, Rico, Ferri, Gabriele, LePage, Kevin D., Nicoli, Monica, Willett, Peter, Braca, Paolo, Win, Moe Z.
This paper addresses the problem of multitarget tracking using a network of sensing agents with unknown positions. Agents have to both localize themselves in the sensor network and, at the same time, perform multitarget tracking in the presence of cl
Externí odkaz:
http://arxiv.org/abs/2108.02573
A new algorithm for 3D localization in multiplatform radar networks, comprising one transmitter and multiple receivers, is proposed. To take advantage of the monostatic sensor radiation pattern features, ad-hoc constraints are imposed in the target l
Externí odkaz:
http://arxiv.org/abs/2104.11209
Autor:
Soldi, Giovanni, Forti, Nicola, Gaglione, Domenico, Braca, Paolo, Millefiori, Leonardo M., Marano, Stefano, Willett, Peter, Pattipati, Krishna
Publikováno v:
IEEE Communications Magazine, vol. 59, no. 9, pp. 16-22, 2021
The COVID-19 pandemic has, worldwide and up to December 2020, caused over 1.7 million deaths, and put the world's most advanced healthcare systems under heavy stress. In many countries, drastic restrictive measures adopted by political authorities, s
Externí odkaz:
http://arxiv.org/abs/2101.04620
Publikováno v:
IEEE Transactions on Aerospace and Electronic Systems, vol. 57, no. 6, pp. 4329-4346, 2021
Data-driven methods open up unprecedented possibilities for maritime surveillance using Automatic Identification System (AIS) data. In this work, we explore deep learning strategies using historical AIS observations to address the problem of predicti
Externí odkaz:
http://arxiv.org/abs/2101.02486