Vessel Trajectory Data Compression Based on Course Alteration Recognition

Guest Editor Yuantao Teng (Jilin Normal University, Shiping City, China)

The vessel trajectory data from automatic identification system (AIS) is massive and contains much redundant information, which makes it difficult to use the data in research and applications. To solve the problem, a data compression algorithm for vessel trajectory was proposed based on vessel course alteration recognition. To recognize the vessel course alteration, the characteristics of trajectory during vessel course alteration were analyzed. It was found that the vessel course alteration on trajectory was similar to the corner on line, so the corner detection algorithm was referenced. Then the representative point of course alteration on trajectory was determined and kept. Other points on trajectories were deleted as redundant points, thus the data of vessel trajectory was compressed. In order to compare the performance of this algorithm with traditional compression algorithms, experiments were implemented base on actual trajectories of vessels from AIS. The results show that the data compression ratio of this algorithm is higher than traditional compression algorithms in vessel trajectory data compression. And this algorithm has a strong adaptability to different voyages of trajectories. The experiments dem

Journal: International Journal of Simulation: Systems, Science & Technology, IJSSST V17

Published: Jan 7, 2016

DOI: 10.5013/IJSSST.a.17.01.05