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Full-Text Articles in Engineering
High-Resolution Bottom-Loss Estimation Using The Ambient-Noise Vertical Coherence Function, Lanfranco Muzi, Martin Siderius, Jorge E. Quijano, Stan E. Dosso
High-Resolution Bottom-Loss Estimation Using The Ambient-Noise Vertical Coherence Function, Lanfranco Muzi, Martin Siderius, Jorge E. Quijano, Stan E. Dosso
Electrical and Computer Engineering Faculty Publications and Presentations
The seabed reflection loss (shortly "bottom loss") is an important quantity for predicting transmission loss in the ocean. A recent passive technique for estimating the bottom loss as a function of frequency and grazing angle exploits marine ambient noise (originating at the surface from breaking waves, wind, and rain) as an acoustic source. Conventional beamforming of the noise field at a vertical line array of hydrophones is a fundamental step in this technique, and the beamformer resolution in grazing angle affects the quality of the estimated bottom loss. Implementation of this technique with short arrays can be hindered by their …
Eigenvector Pruning Method For High Resolution Beamforming, Jorge E. Quijano, Lisa M. Zurk
Eigenvector Pruning Method For High Resolution Beamforming, Jorge E. Quijano, Lisa M. Zurk
Electrical and Computer Engineering Faculty Publications and Presentations
This paper introduces an eigenvector pruning algorithm for the estimation of the signal-plus-interference eigenspace, required as a preliminary step to subspace beamforming. The proposed method considers large-aperture passive array configurations operating in environments with multiple maneuvering targets in background noise, in which the available data for estimation of sample covariances and eigenvectors are limited. Based on statistical properties of scalar products between deterministic and complex random vectors, this work defines a statistically justified threshold to identify target-related features embedded in the sample eigenvectors, leading to an estimator for the signal-bearing eigenspace. It is shown that data projection into this signal …