A hierarchical clustering architecture is proposed to deal with the problem of jamming environment classification when multiple noise-like jammers are possibly present. Assuming the availability of clutter-free multichannel data, a two-level hierarchical procedure is devised to unveil the presence of clusters containing range cells experiencing the same jamming interference as the cell under test. Level 1 relies on the use of covariance smoothing and model-order selection rules to make inference on the number of jamming signals affecting each range bin within the radar range swath. Level 2 allows to discriminate among possible different interfering scenarios characterized by the same number of jammers via an unsupervised learning clustering fed by a suitable feature set. At the analysis stage, the performance of the devised architecture is investigated over simulated and measured data (via software-defined radio devices) to highlight the benefits of the approach.

A Clustering Approach for Jamming Environment Classification / Carotenuto, V.; De Maio, A.. - In: IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS. - ISSN 0018-9251. - 57:3(2021), pp. 1903-1918. [10.1109/TAES.2021.3050655]

A Clustering Approach for Jamming Environment Classification

Carotenuto V.;De Maio A.
2021

Abstract

A hierarchical clustering architecture is proposed to deal with the problem of jamming environment classification when multiple noise-like jammers are possibly present. Assuming the availability of clutter-free multichannel data, a two-level hierarchical procedure is devised to unveil the presence of clusters containing range cells experiencing the same jamming interference as the cell under test. Level 1 relies on the use of covariance smoothing and model-order selection rules to make inference on the number of jamming signals affecting each range bin within the radar range swath. Level 2 allows to discriminate among possible different interfering scenarios characterized by the same number of jammers via an unsupervised learning clustering fed by a suitable feature set. At the analysis stage, the performance of the devised architecture is investigated over simulated and measured data (via software-defined radio devices) to highlight the benefits of the approach.
2021
A Clustering Approach for Jamming Environment Classification / Carotenuto, V.; De Maio, A.. - In: IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS. - ISSN 0018-9251. - 57:3(2021), pp. 1903-1918. [10.1109/TAES.2021.3050655]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/884733
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