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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Clustering Approach for Jamming Environment Classification


    Contributors:


    Publication date :

    2021-06-01


    Size :

    2710849 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    GPS Jamming in a Laboratory Environment

    Rash, G. / Institute of Navigation | British Library Conference Proceedings | 1997


    A Deep Neural Network Approach for Classification of GNSS Interference and Jamming

    Mehr, Iman Ebrahimi / Dovis, Fabio | IEEE | 2025

    Free access

    IMECE2002-32478 Clustering, Jamming and Segregation in Cohesive Granular Materials

    Samadani, A. / Blair, D. L. / Kudrolli, A. et al. | British Library Conference Proceedings | 2002


    Radar Network Target Tracking in a Jamming Environment

    Song, X. / Sun, Z. / IEEE; Dayton Section et al. | British Library Conference Proceedings | 1996