A N-order Interpolated Variance Estimator Algorithm for Fusion of Inland Waterway Crowd-sourced Bathymetry Data

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A N-order Interpolated Variance Estimator Algorithm for Fusion of Inland Waterway Crowd-sourced Bathymetry Data

Wang, Dejun / Wang, Yanxia / Liang, Shan / Ye, Chengyang

Periodic bathymetry surveys for inland waterway are essential to provide sufficient width and depth for a safe passage of ships. High-quality bathymetry data is usually collected with an automatic survey system. Acquisition of bathymetry data for inland waterway, however, is very costly, time-consuming and complicated. Fortunately, crowdsourcing based bathymetry provides a cheap and fast solution to obtain enough soundings for describing something of the great variety of the silt deposition and river bed deformation. However, these unprofessional data must undergo significant quality control measures to improve data quality. In this paper, the fusion of some independent repeating soundings measured by crowdsourcing is discussed. The 2-order interpolated variance estimator (IVE) algorithm in variance estimation of data fusion is extended to n-order IVE algorithm for achieving more effective fusion of repeating data. The feasibility and effectiveness of the proposed algorithm are also supported by simulations and experiments.