Abstract
For the safe operation of a ship, it is necessary to detect and track nearby ships, called target ships, accurately. To track the target ships, Kalman filters (KF) have been widely used. However, when the system model of the KF, which predicts the motion of the system, may be different from the actual motion of the target ships, the accuracy could decrease. To solve this problem, the Interactive Multi-Model (IMM) method can be adopted, which follows several system models at the same time, but the number of system models that the IMM implements is limited. Therefore, a method that can analyze the actual dynamic behavior of the target ships and track accurately the target ships based on historical data is required. Recently, deep learning has been applied to tracking methods to solve existing problems and improve accuracy. For tracking, the Long-Short Term Memory (LSTM) method is being mainly applied and shows better performance in the marine environment where the motion characteristics of the target ships could change.