A Method for Estimating Ship Surface Wind Parameters by Combining Anemometer and X-Band Marine Radar Data

The steady airflow field on a ship is affected by structure and motion and challenged by phenomena such as the low measurement accuracy of the wind field caused by the occlusion of the anemometer. In this work, an improvement in the accuracy of wind measurements affected by structure is proposed, and a method for combining anemometer and X-band marine radar (RCRF) data is designed to further obtain wind parameters. The first step is to use the multivariate bias strategy to achieve the optimal layout of multiple anemometers based on computational fluid dynamics (CFD) numerical simulation data. Then, random forest (RF) is employed to train the wind parameter estimation model. Finally, the wind parameters are optimally estimated by combining the anemometer with the X-band radar. Under the ideal simulation, noise, and temporal uncertainty combined with anemometer noise conditions, the RCRF algorithm performance is evaluated. Compared with the bias correction combination four-anemometer weighted fusion algorithm (FAF-BC) and the BP neural network algorithm for radar wind measurement combination (RCBP), the mean errors in wind direction and speed are reduced by 1.99 degrees and 6.99% at most. The maximum errors are reduced by 14.46 degrees and 15.81% at most, respectively.

Automated summary

This study proposes a method to improve wind measurement accuracy affected by ship structure, combining anemometer and X-band radar data to obtain more accurate wind parameters. Using multivariate bias strategy and random forest model to optimize the layout of multiple anemometers and train wind parameter estimation model, improving wind parameter estimation accuracy.