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To maintain the existing systems of ships and introduce autonomous operation technology, it is necessary to improve situational awareness through the sensor fusion of the automatic identification system (AIS) and automatic radar plotting aid (ARPA), which are installed sensors. This study proposes an algorithm for determining whether AIS and ARPA signals are sent to the same ship in real time. To minimize the number of errors caused by the time series and abnormal phenomena of heterogeneous signals, a tracking method based on the combination of the unscented Kalman filter and probabilistic data association filter is performed on ARPA radar signals, and a position prediction method is applied to AIS signals. Especially, the proposed algorithm determines whether the signal is for the same vessel by comparing motion-related components among data of heterogeneous signals to which the corresponding method is applied. Finally, a measurement test is conducted on a training ship. In this process, the proposed algorithm is validated using the AIS and ARPA signal data received by the voyage data recorder for the same ship. In addition, the proposed algorithm is verified by comparing the test results with those obtained from raw data. Therefore, it is recommended to use a sensor fusion algorithm that considers the characteristics of sensors to improve the situational awareness accuracy of existing ship systems.

In this paper, a collision avoidance algorithm based on predictive probability using the Kalman filter is proposed. Existing algorithms are based on the Time to the Closest Point of Approach (TCPA) and Distance to the Closest Point of Approach (DCPA) derived from geometric data between own ship and target ships using current states (heading angle and velocity). When compared to these approaches, the proposed algorithm is capable of predicting the states and easy to plan optimal path by considering the uncertainty in avoidance situation based on the number of ships. In this study, an Unscented Kalman Filter (UKF) is used due to the nonlinearities in the state variables to be predicted. By using the predicted state variables of the ships calculated via the UKF's prediction step, the positions of the number of ships after a certain time are derived as a predictive probability. Then, the validation of the proposed algorithm is examined by planning the optimal path in the collision avoidance simulations by applying the predictive probability.