High resolution matched-field source localization based on sparse-reconstruction

Abstract

Matched-field processing (MFP) for source localization usually experiences shortcomings such as low resolution and high computational workload. In this paper, a high resolution matched-field source localization method based on sparse reconstruction algorithms is presented. The underwater source localization problem is represented by solving an underdetermined linear equation. By enforcing the spatial sparsity of source signals, the signal strength on a specified grid is evaluated using sparse reconstruction algorithms. Focusing on the case of multiple snapshots, the system's equation based on the data correlation matrix is established, which increases the ratio of measurements to sparsity (RMS) and reduces the problem dimensionality to the minimum. Besides, the system equation can be equivalent to a Bartlett processor, thus the proposed method can achieve robust estimation as effectively as Bartlett but with better resolution.