Kavli Affiliate: Wei Gao
| First 5 Authors: Xiaolei Li, Xiaolei Li, , ,
| Summary:
This paper proposes an orthogonality-constrained modal search (OCMS) method
for estimating modal wavenumbers and modal depth functions using a vertical
linear array (VLA). Under the assumption of a known sound speed profile, OCMS
leverages the orthogonality of distinct modal depth functions to extract both
the modal depth functions and their corresponding wavenumbers, even when the
VLA and a monochromatic sound source remain stationary.The performance of OCMS
is evaluated through numerical simulations under varying signal-to-noise ratios
(SNRs), different VLA apertures, varying numbers of VLA elements, VLA tilt and
sound speed profile (SSP) uncertainty. The results demonstrate that OCMS is
robust against noise, VLA aperture variations, and changes in the number of VLA
elements, meanwhile, the algorithm maintains reliable performance when SSP
uncertainty < 1 m/s and VLA tilt angle <5deg. Furthermore, the effectiveness
of OCMS is validated using SwellEx96 experimental data. The relative error
between the modal wavenumbers derived from experimental data and those computed
via Kraken is on the order of $10^-4$.
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