Graph-based Matched Field Localization for an Underwater Source

TL;DR

This paper introduces a novel graph signal processing framework for underwater source localization, enhancing the accuracy and robustness of traditional matched field processing methods.

Contribution

It combines graph signal processing with matched field processing, using a graph Fourier transform for improved underwater source localization.

Findings

Graph-based MFP outperforms conventional processors in accuracy.

The method demonstrates robustness in simulated underwater environments.

Simulation results validate the effectiveness of the proposed approach.

Abstract

Matched Field Processing (MFP) locates the underwater sources by matching the received data with the replica vectors, which could be regarded as a generalized beamformer. In this paper, the MFP method is combined with a recently developed framework -- Graph Signal Processing (GSP) method. Following the paradigm of GSP, a spatial adjacency matrix is constructed for the arbitrary distributed sensors based on the Green's function, then the source is located by utilizing the graph Fourier transform. The simulation results illustrate that the Graph-based MFP outperforms the the conventional MFP processors -- the Bartlett processor and the Minimum Variance processor -- for its good accuracy and robustness.