Performance assessment of vessel dynamic models for long-term prediction using heterogeneous data

Ship traffic monitoring is a foundation for many maritime security domains, and monitoring system specifications underscore the necessity to track vessels beyond territorial waters. However, vessels in open seas are seldom continuously observed. Thus, the problem of long-term vessel prediction becomes crucial. This paper focuses attention on the performance assessment of the Ornstein-Uhlenbeck (OU) model for longterm vessel prediction, compared with usual and well-established nearly constant velocity (NCV) model. Heterogeneous data, such as automatic identification system (AIS) data, high-frequency surface wave radar data, and synthetic aperture radar data, are exploited to this aim. Two different association procedures are also presented to cue dwells in case of gaps in the transmission of AIS messages. Suitable metrics have been introduced for the assessment. Considerable advantages of the OU model are pointed out with respect to the NCV model. Index Terms-Automatic identification system (AIS), high-frequency surface wave (HFSW) radar, long-term prediction, maritime surveillance, nearly constant velocity (NCV) model, Ornstein-Uhlenbeck (OU) process, synthetic aperture radar (SAR).