A variational technique for source localization based on a sparse signal reconstruction perspective

We propose a novel non-parametric technique for source localization with passive sensor arrays. Our approach involves formulation of the problem in a variational framework where regularizing sparsity constraints are incorporated to achieve super-resolution and noise suppression. Compared to various source localization schemes, our approach offers increased resolution, significantly reduced sidelobes, and improved robustness to limitations in data quality and quantity. We demonstrate the effectiveness of the method on simulated data.