From Kelvin Wave Patterns to Ship Displacement: An Inverse Prediction Framework Based on a Hull Form Database

Abstract

The estimation of a ship’s displacement volume, ∇, from remote sensing data is of considerable practical value for maritime surveillance and vessel characterization. This paper introduces a practical framework for the inverse estimation of displacement volume from Kelvin ship waves, building upon a prior study through two key extensions. First, the wave amplitude function is recovered using Fourier series expansions combined with the stationary phase method. The displacement volume is then estimated via a two-step procedure: an initial estimate is obtained by identifying a hull with similar amplitude characteristics from a database, followed by a refinement that incorporates discrepancies between the target and candidate wave amplitude functions. In the case studied, the proposed approach achieves a prediction error of 4.02%, demonstrating its potential for non-invasive extraction of hull information from remote sensing data.

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