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
IEEE Transactions on Aerospace and Electronic Systems ; 57 , 3 ; 1903-1918
2021-06-01
2710849 byte
Article (Journal)
Electronic Resource
English
GPS Jamming in a Laboratory Environment
British Library Conference Proceedings | 1997
|A Deep Neural Network Approach for Classification of GNSS Interference and Jamming
IEEE | 2025
|IMECE2002-32478 Clustering, Jamming and Segregation in Cohesive Granular Materials
British Library Conference Proceedings | 2002
|Radar Network Target Tracking in a Jamming Environment
British Library Conference Proceedings | 1996
|