Probabilistic Modelling of Target Drift in Marine Disasters Using Ocean Currents From High-Frequency Radars

Abstract

The issue of target drift prediction based on ocean current from high-frequency (HF) ground-wave radar was studied in this paper. A large number of unpowered drift observation experiments for a class of South China Sea characteristic offshore fishing vessel was carried out in the range of radar radiation. The calculation of sub-grid velocity in the test area was based on the comparison between the HF radar data and the meteorological observation data of the buoy at sea. The two observation data also have good consistency in the matching of time and space. To analyze the temporal characteristics of diffusion velocity, the sample autocorrelation and the partial autocorrelation function of a sub-grid velocity were calculated separately, and a sub-grid model based on the ARMA model was proposed. Firstly, trajectory simulation based on the Runge-Kutta method was used to preliminarily verify the improvement of target drift prediction accuracy by ARMA model. In addition, the Monte-Carlo method was used to calculate and compare the prediction range of different sub-grid velocity models, and the ARMA model with different continuous prediction time steps was studied. Compared with the traditional sub-grid velocity model, the ARMA model with short-term continuous prediction can significantly improve the performance of target drift prediction model in terms of mean prediction error and separation. However, ARMA model is also limited by its continuous prediction step size to a large extent. On the one hand, this study verifies that the ARMA model with short-term continuous prediction improves the performance of target drift prediction model. On the other hand, the results further demonstrated that measured data provided by the HF radar, combined with other continuous ocean observation data, are of value for trajectory analysis of drift targets.