Parametric optimization of adaptive wave filter using an objective function

Wave filtering is one of the mandatory features of the state estimators in a dynamic position system. The optimization of statistical parameters of these state estimators can be done by covariance matching algorithms and appropriate objective (cost) functions. The proposed cost function has predictive behavior, based on some tuning parameters, which control the quality of wave filtering. These parameters assure convergence of the solution and consistent results in different adaptive algorithms based on the Kalman filter framework as AKF, AEKF, and AUKF.