Sensors, Vol. 19, Pages 4365: L-VTP: Long-Term Vessel Trajectory Prediction Based on Multi-Source Data Analysis

Sensors, Vol. 19, Pages 4365: L-VTP: Long-Term Vessel Trajectory Prediction Based on Multi-Source Data Analysis Sensors doi: 10.3390/s19204365 Authors: Chao Liu Shuai Guo Yuan Feng Feng Hong Haiguang Huang Zhongwen Guo With the rapid development of marine IoT (Internet of Things), ocean MDTN (Mobile Delay Tolerant Network) has become a research hot spot. Long-term trajectory prediction is a key issue in MDTN. There are no long-term fine-grained trajectory prediction methods proposed for ocean vessels because a vessel’s mobility pattern lacks map topology support and can be easily influenced by the fish moratorium, sunshine duration, etc. A traditional on-land trajectory prediction algorithm cannot be directly utilized in this field because trajectory characteristics of ocean vessels are far different from that on land. To address the problem above, we propose a novel long-term trajectory prediction algorithm for ocean vessels, called L-VTP, by utilizing multiple sailing related parameters and K-order multivariate Markov Chain. L-VTP utilizes multiple sailing related parameters to build multiple state-transition matrices for trajectory prediction based on quantitative uncertainty analysis of trajectories. Trajectories’ sparsity of ocean vessels results in a critical state missing problem of a high-order state-transition matrix. L-VTP automatically traverses other matrices in a specific sequence in terms of quantitative uncertainty ...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research