Abstract
Sparse Iterative Covariance-based Estimation (SPICE) is a typically hyperparameter free algorithm for DOA (Direction Of Arrival) estimation. In this paper, we modify the SPICE criterion to form a weighted ℓ<inf>2</inf> minimization. Instead of the weighted SPICE with the single weighting value, the proposed algorithm exploits multiple and different weighting values to further enhance the sparsity of the solution. Numerical examples demonstrate that the proposed algorithm produces better accuracy of DOA estimation and higher spatial resolution compared to several typical methods of sparse signal recovery.
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