A sparse approach for DOA estimation with a multiple spatial invariance sensor array

In this paper, we introduce a sparse direction-of-arrival (DOA) estimation algorithm for sensor arrays presenting multiple scales of spatial invariance. We exploit the Khatri-Rao structure of the over-complete steering vector dictionary, corresponding to this array geometry, in order to devise a computationally efficient sparse estimation approach. This approach is based on an iterative refinement and pruning strategy of the dictionary. We show, in numerical simulations, that our approach outperforms the state-of-the-art approach based on a Candecomp/Parafac (CP) decomposition, proposed by Miron et al. in 2015.