A GENERALIZED MODEL OF A VESSEL DEAD RECKONING BASED ON NEURAL NETWORKS

Abstract

The task of predicting the vessel kinematic parameters arises in the dead reckoning mode of its track calculation. This task is solved in the framework of the traditional approach based on using the ordinary differential equations. As a rule, while constructing these equations, the difficulties arise. The difficulties result from selection of the algorithms which are used for the certain forces calculation. These algorithms are not universal, and their working ability could not be guaranteed for all sailing conditions. The task of predicting the vessel kinematic parameters can be performed as the task of approximation of multiple variables functions. Neural networks are known as universal algorithms of such approximation. In the paper, a generalized model of the vessel dead reckoning on the basis of neural networks is performed. An algorithm of its operation is performed as well. The base of the model is formed with deep neural nets which are constructed through a cascade connection between two-layered (shallow) networks. There are two types of neural networks in the structure of the model: main and auxiliary nets. The main nets predict kinematic parameters using the information about force influences on the vessel. The auxiliary nets use the information about only the ship kinematic history. The model configuration allows two modes of operation. The first mode is used when dead reckoning is performed in the usual way. The second mode is used when the reiable information from a log or (and) gyrocompass is unavailable. In the case of both sensors failure the model reproduces the vessel dynamics in the horizontal plane (with three degree of freedom) thus substituting the system of corresponding differential equations. The input signals of the main nets are formed in accordance with the configuration vector of the model. The capabilities of learning the model and its using in practice are also analysed.