Robust Subspace Clustering for Radar Detection
| Autor: | M. N. El Korso, Hamid Krim, Ashkan Panahi, Arnaud Breloy |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Signal processing
Covariance matrix Computer science 020206 networking & telecommunications 02 engineering and technology Linear subspace Matrix (mathematics) Factorization 0202 electrical engineering electronic engineering information engineering Clutter 020201 artificial intelligence & image processing Algorithm Subspace topology Statistical hypothesis testing Sparse matrix |
| Zdroj: | EUSIPCO |
| Popis: | Target detection embedded in a complex interference background such as jamming or strong clutter is an important problem in signal processing. Traditionally, statistical adaptive detection processes are built from a binary hypothesis test performing on a grid of steering vectors. This usually involves the estimation of the noise-plus-interference covariance matrix using i.i.d. samples assumed to be target-free. Moving from this paradigm, we exploit the fact that the interference (clutter and/orjammers) lies in a union of low-dimensional subspaces. Hence, the matrix of concatenated samples can be modeled as a sum of low-rank matrices (union of subspaces containing interferences) plus a sparse matrix times a dictionary of steering-vectors (rep-resenting the targets contribution). Recovering this factorization from the observation matrix allows to build detection maps for each sample. To perform such recovery, we propose a generalized version of the robust subspace recovery via bi-sparsity pursuitalgorithm [1]. Experimental results on a real data set highlight the interest of the approach. |
| Databáze: | OpenAIRE |
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