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SSG Seminar Abstract

A Variational Technique for Source Localization Based on a Sparse Signal Reconstruction Perspective

Mujdat Cetin
SSG Postdoctoral Associate

Source localization using sensor arrays has been an active research area, playing a fundamental role in many applications involving electromagnetic, acoustic, and seismic sensing. In this talk, we outline a research effort we have recently started in source localization, describe a non-parametric technique we have developed, and present some preliminary results.

Our approach involves formulation of the problem in a variational framework, where regularizing sparsity constraints are incorporated to achieve superresolution source localization and noise suppression. We present a computationally efficient numerical method based on half-quadratic regularization for the solution of the resulting optimization problems in this framework. Compared to currently existing source localization methods, our approach offers increased resolution, reduced sidelobes, and improved robustness to limitations in data quality and quantity. We demonstrate the effectiveness of the method on simulated data. We will discuss various issues about the current approach and some potential extensions which are subjects of our current research.

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