Ship Track Regression Based on Support Vector Machine

The noise problem is crucial in modeling ship maneuvering motion function based on sampling tracks by conducting self-propulsion model tests. In general, the normal noise in the data is tolerated and deposed properly. While abnormal noise and outliers might accumulate errors, they are not accepted during the ship motion function training. In this paper, we show that the problems of variant Gaussian noise and outliers can be overcome using a support vector regression (SVR) method. The solution of SVR is given as a formula using sequential minimal optimization training algorithm. Simulations were conducted to validate the SVR method in dealing with variant Gaussian noise polluted ship tracks compared to polynomial and Fourier regression methods based on the known maneuvering motion function of the ship Mariner. Finally, the promising performance of the SVR method in deposing outliers and regressing polluted ship tracks is demonstrated. Here, the polluted ship tracks were recorded using an ultrasonic positioning system by conducting set-sail and circular tests in a towing tank.