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Communication Dans Un Congrès Année : 2019

Modified Sparse Subspace Clustering for Radar Detection in Non-stationary Clutter

Résumé

Detecting targets embedded in a noisy environment is an important topic in adaptive array processing. In the traditional statistical framework, this problem is addressed through a binary hypothesis test, which usually requires the estimation of side parameters from secondary data. The latter are assumed to be homogeneous and target-free, which is in practice questionable. Indeed, secondary data are usually corrupted by radar clutters and/or jammers which can be non-stationary and locally low rank. Fortunately, the latter behaviors can be well acknowledged by a union-of-subspaces model. In this work, we propose a modified subspace clustering model which can be solved using convex optimization algorithms. In the context of multiple sparse target detection and localization, a comparison is performed with various robust detection methods exhibiting advantages and drawbacks of the proposed one.
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Dates et versions

hal-02292685 , version 1 (20-09-2019)
hal-02292685 , version 2 (14-10-2019)

Identifiants

Citer

Bruno Meriaux, Arnaud Breloy, Chengfang Ren, Mohammed Nabil El Korso, Philippe Forster. Modified Sparse Subspace Clustering for Radar Detection in Non-stationary Clutter. 8th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 2019), Dec 2019, Le Gosier, Guadeloupe, France. ⟨10.1109/camsap45676.2019.9022511⟩. ⟨hal-02292685v2⟩
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