Depth estimation for broadband sources with a vertical line array in deep water.

In deep water, deploying a short vertical line array (VLA) is an effective way for source localization. In the past decade, most studies focused on localizing sources at the short to moderate ranges in the reliable acoustic path or the direct arrival zone (DAZ), with a VLA deployed near the ocean bottom. Little work has been done for the end part of the DAZ and the zones outside the DAZ. In addition, a VLA deployed at other depths rather than near the bottom is rarely studied. This paper proposes a near-surface source depth estimation method by matching the measured time delay with a library of modeled values under different source depths calculated by a simple formula. This method is suitable for zones, which contains two paths (one is reflected from the sea surface) with very close arrival angles, of a VLA deployed not only near the bottom, but also at other depths of the water column. Source depth estimation strategy for the end part of each zone, which faces the problem of poor depth resolution, is also analyzed. Simulation and experimental data of the airgun and explosive sources in the South China Sea are used to demonstrate the method.

Passive source localization based on multipath arrival angles with a vertical line array using sparse Bayesian learning.

In deep water, multipath time delays or frequency-domain interference periods of the acoustic intensity combined with multipath arrival angles are typically used for source localization. However, depth estimate is hard to achieve for a narrowband source at a remote part of the direct arrival zone as the required bandwidth increases with the source range. In this paper, a passive source localization method with a vertical line array, suitable for both broadband and narrowband sources, is proposed. Based on the variation trends of multipath angles with source range and depth, source localization is achieved by only matching the measured angles of the direct path and surface-reflected path with model-based values of a predefined grid of potential source locations. Considering the angle resolution limited by the array aperture and the presence of coherent multipath, sparse Bayesian learning is used and compared with the conventional beamforming and the minimum-variance distortionless-response beamforming to resolve and estimate the multipath angles. Simulations and experimental data of explosive sources collected by a vertical line array in the South China Sea are carried out to illustrate the method and demonstrate the performance.