Research and Application of Submersible Position Prediction Model Based on Kalman Filtering

In this study, a positioning prediction model is proposed for the submersible position prediction problem based on the Kalman filter algorithm. First, the problem is analyzed to determine the uncertainty factors involved in the prediction of the position of the submersible, such as the propulsion of the submersible, ocean currents, seawater density and seabed topography. Subsequently, a position prediction model based on Kalman filtering algorithm is established, including the steps of state definition, system dynamic model, observation model, definition of initial state and covariance matrix, Kalman gain calculation, state update, and state prediction at the next moment. For the equipment requirements involved in the model building process, the factors affecting the position prediction of the submersible are analyzed and the corresponding equipment requirements are proposed. Finally, Python software is utilized for model fitting and prediction, and the fitting accuracy of the model is evaluated by mean square error and mean absolute error, and the results show that the model has a good fitting effect.