1991
DOI: 10.1121/1.2029699
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Focused beamforming for vertical line arrays in near-field bottom scattering experiments

Abstract: A fine-scale acoustic bottom scattering experiment is planned for the summer of 1993 on the Mid-Atlantic Ridge flank just north of the Kane Fracture Zone as a part of the Acoustic Reverberation Special Research Program (ARSRP). The site (or sites) of the detailed experiment will be about 5 × 5 km. The experimental design includes the use of four vertical line arrays moored near the bottom within the site. Several factors complicate the geometry of the experiment, one of which is that the scattering patch will … Show more

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“…Equations (15) and (16) gives the uniform sparse representation of signals where both far-field targets and near-field interferences exist. The matrix form with the multiple measurement vector can be rewritten as:…”
Section: Unified Sparse Representation Model For Far-and Near-field Smentioning
confidence: 99%
See 1 more Smart Citation
“…Equations (15) and (16) gives the uniform sparse representation of signals where both far-field targets and near-field interferences exist. The matrix form with the multiple measurement vector can be rewritten as:…”
Section: Unified Sparse Representation Model For Far-and Near-field Smentioning
confidence: 99%
“…To apply these algorithms to near-field interference suppression, the accurate measurement or estimation of interference is required as the filters' input, which can be very difficult to secure in practice. In order to apply the ANC algorithms to near-field interference suppression, serval focused beamformers have been developed to locate and extract near-field sources [15][16][17], but some important parameters such as the side-lobe level, array gain, and width of the main lobe are difficult to compromise. Interference source characteristics, such as position and intensity can be time-varying, which leads to impure reference signal extraction, further limiting the practical application of the adaptive interference cancellation algorithm.…”
Section: Introductionmentioning
confidence: 99%