Abstract
Passive multiple sound source localization is a challenging problem in underwater acoustics, especially for a short hydrophone array in the deep ocean. Several attempts have been made to solve this problem by applying compressive sensing (CS) techniques. In this study, one greedy algorithm in CS theory combined with a spatial filter was developed and applied to a two-source localization scenario in the deep ocean. This method facilitates localization by utilizing the greedy algorithm with a spatial filter at several iterative loops. The simulated and experimental data suggest that the proposed method provides a certain localization performance improvement over the use of the Bartlett processor and the greedy algorithm without a spatial filter. Additionally, the effects on the source localization caused by factors such as the array aperture, number of hydrophones or snapshots, and signal-to-noise ratio (SNR) are demonstrated.
Highlights
Sound source localization with a short vertical line array (VLA) of hydrophones is challenging in the deep ocean for the reason that a small aperture and a limited number of sensors cannot sample enough acoustic modes [1,2]
The aim of this paper is to propose a matched field processing (MFP) framework based on compressive sensing (CS) applied to the localization of two sources at single frequency with a short aperture array in the deep ocean
The RAMP algorithm combined with matrix filter (RAMPMF) method is applied to a simulated dataset and experimental data to compare the different processors and demonstrate the performance
Summary
Sound source localization with a short vertical line array (VLA) of hydrophones is challenging in the deep ocean (the array aperture is less than one-tenth the ocean depth) for the reason that a small aperture and a limited number of sensors cannot sample enough acoustic modes [1,2]. The aim of this paper is to propose a matched field processing (MFP) framework based on compressive sensing (CS) applied to the localization of two sources at single frequency with a short aperture array in the deep ocean. There are a large number of published studies that propose methods for multiple source localization, which can generally be categorized into three types. The first type use the eigenvectors of the cross-spectral density matrix (CSDM) to estimate the positions of uncorrelated sources [3,4,5,6]
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