摘要
海上环境不同于陆地,其不受道路、轨道的限制和受表面风流场多因素影响,其目标的运动轨迹更显杂乱,给海上目标的轨迹分析带来挑战。提出一种基于高斯混合模型的海上浮标轨迹的聚类算法。该算法将高斯混合模型应用于漂移浮标的复杂不规则轨迹的聚类,能够有效消除轨迹中异常点的影响。仿真实验表明针对浮标漂移轨迹GMM算法较K-means算法更优,鲁棒性更好。该研究成果可应用于海上搜救、航路规划等领域。
The sea environment is different from the land trajectory has a certain regularity, a variety of factors are not controlled, resulting in mari- time trajectory analysis is more difficult. So, presents an algorithm for sea buoy trajectory clustering based on Gaussian Mixture Model. The algorithm can be applied to the unrestricted and complex trajectory of the sea buoy, and the irregular trajectory. And it can effectively elimi- nate the influence of abnormal points in the trajectory. The experimental results show that the Gaussian mixture model clustering algorithm has higher reliability than K-means. The research results can be applied to such fields as maritime search and rescue, route planning and
出处
《现代计算机》
2017年第24期3-5,8,共4页
Modern Computer
关键词
聚类
高斯混合模型
浮标
漂移轨迹
Clustering
Gaussian Mixture Model
Sea Buoy
Drafting Trajectory
