Direction-of-arrival estimation using sparse variable projection optimization

We propose a new low complexity direction-of-arrival (DOA) estimation method based on sparse variable projection (SVP) optimization. This method estimates an indicative sparse vector that indicates the locations of DOA from each visual sources corresponding to DOA sampling space and is particular useful to simplify the multiple measurement vector (MMV) problem as a single indicative sparse vector recovery problem. The indicative sparse vector can be recovered by adding additional sparsity measure information. We use ℓ_p(p ≤ 1) norm and smoothed approximate ℓ₀ norm to regularize the SVP function, so that we can formulate the SVP optimization as an unconstrained optimization problem and we solve it efficiently using quasi-Newton method. The experimental results demonstrate that our method has much lower complexity by comparing with a standard Regularized M-FOCUSS algorithm.