Processing of Heading Data with Machine Learning for MBES Survey

The paper presents research on using machine learning algorithms for heading signal smoothing recorded during MBES surveys. Several numerical methods, typically used for time series smoothing and prediction in Data Science applications are tested, like moving average, Gauss filter, Holt Winters filter and Wittaker filter. Additionally, recurrent neural networks are analyzed. Data from real use cases are used and parameters of the methods are verified. The methods are validated against smoothing performance (with variance analysis) and against original function fitting (with RMSE), allowing the qualitative and quantitative assessment. Open source python libraries are used. The results shows efficiency of such approach for this problem.