Broadband underwater multi-source localization with a computationally efficient coherent OMP algorithm

In this paper, we propose a broadband coherent matched-field processing (MFP) algorithm to solve multi-source localization problems in shallow water scenarios. The proposed algorithm combines the matched-phase coherent processor with the sparse recovery technique from compressive sensing (CS) theory. A greedy sparse recovery algorithm is adopted to iteratively locate multiple sources using a matched-phase coherent processor. At each iteration of the greedy algorithm, the data is processed coherently using the phase descent search (PDS) algorithm, rather than the incoherent methods used in many sparse recovery algorithms, such as the classical orthogonal matching pursuit (OMP) algorithm. The phase shifts between different frequencies are estimated and compensated, such that the performance can be greatly enhanced. The proposed algorithm is applied to simulated data, synthesized data, and data collected in the SWellEx-96 shallow water experiment. The result provides sparse localization information that matches the ground truth source locations in the simulation and the source trajectory calculated from the Global Positioning System (GPS) information from the experiment.