EM-based algorithm for unsupervised clustering of measurements from a radar sensor network
Yan, Linjie and Addabbo, Pia and Fiscante, Nicomino and Clemente, Carmine and Hao, Chengpeng and Giunta, Gaetano and Orlando, Danilo (2024) EM-based algorithm for unsupervised clustering of measurements from a radar sensor network. IEEE Transactions on Aerospace and Electronic Systems. pp. 1-15. ISSN 0018-9251 (https://doi.org/10.1109/TAES.2024.3448390)
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Abstract
This paper deals with the problem of clustering data returned by a radar sensor network that monitors a region where multiple moving targets are present. The network is formed by nodes with limited functionalities that transmit the estimates of target positions (after a detection) to a fusion center without any association between measurements and targets. To solve the problem at hand, we resort to model-based learning algorithms and instead of applying the plain maximum likelihood approach, due to the related computational requirements, we exploit the latent variable model coupled with the expectation-maximization algorithm. The devised estimation procedure returns posterior probabilities that are used to cluster the huge amount of data collected by the fusion center. Remarkably, we also consider challenging scenarios with an unknown number of targets and estimate it by means of the model order selection rules. The clustering performance of the proposed strategy is compared to that of conventional data-driven methods over synthetic data. The numerical examples point out that the herein proposed solutions can provide reliable clustering performance overcoming the considered competitors.
ORCID iDs
Yan, Linjie, Addabbo, Pia, Fiscante, Nicomino, Clemente, Carmine ORCID: https://orcid.org/0000-0002-6665-693X, Hao, Chengpeng, Giunta, Gaetano and Orlando, Danilo;-
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Item type: Article ID code: 90359 Dates: DateEvent23 August 2024Published23 August 2024Published Online1 August 2024AcceptedSubjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Electronic and Electrical Engineering
Strategic Research Themes > Measurement Science and Enabling Technologies
Strategic Research Themes > Ocean, Air and SpaceDepositing user: Pure Administrator Date deposited: 26 Aug 2024 14:55 Last modified: 03 Oct 2024 00:43 URI: https://strathprints.strath.ac.uk/id/eprint/90359