Clustering for Jamming Environment Classification

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 (MOS) 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 data to highlight the benefits of the approach.