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Key techniques for predicting the uncertain trajectories of moving objects with dynamic environment awareness

ISSN号：1001-3695
期刊名称：《计算机应用研究》
时间：0
分类：TP183[自动化与计算机技术—控制科学与工程;自动化与计算机技术—控制理论与控制工程] X-4[环境科学与工程]
作者机构：[1]School of Information Science and Technology,Southwest Jiaotong University,Chengdu 610031,China, [2]Department of Computer Science,University of Illinois at Urbana-Champaign,Urbana 61801,USA, [3]School of Electronics and Information Engineering,Ningbo University of Technology,Ningbo 315016,China
相关基金：supported by the National Natural Science Foundation of China （Nos.61100045 61165013 61003142 60902023 and 61171096）; the China Postdoctoral Science Foundation （Nos.20090461346 201104697）; the Youth Foundation for Humanities and Social Sciences of Ministry of Education of China （No.10YJCZH117）; the Fundamental Research Funds for the Central Universities （Nos.SWJTU09CX035 SWJTU11ZT08）; Zhejiang Provincial Natural Science Foundation of China （Nos.Y1100589 Y1080123）; the Natural Science Foundation of Ningbo China （No.2011A610175）

作者： Shaojie QIAO[1], Xian WANG[1], Lu＇an TANG[2], Liangxu LIU[3], Xun GONG[1]

关键词：预测算法, 环境意识, 移动物体, 技术, 定轨, 环境因素, 贝叶斯网络, 移动通信, trajectory prediction, moving objects databases, dynamic environmental factors, continuous time Bayesian networks

中文摘要：

无线、活动的通讯的新兴的技术使人能积累大量 time-stamped 地点，它出现在一条连续移动目标轨道形式。怎么精确地预言目标的不明确的活动性成为一个重要、挑战性的问题。为在动人的目标数据库的轨道预言的存在算法主要集中于识别经常的轨道模式，并且不考虑必要动态环境因素的效果。在这研究，为与动态环境了解预言动人的目标的不明确的轨道的一个一般纲要被介绍，并且在轨道预言的关键技术详细被探讨。以便精确地预言轨道，一个轨道预言算法基于连续时间，贝叶斯的网络(CTBN ) 被改进并且适用，它拿动态环境因素进完整的考虑。在合成轨道数据上进行的实验验证改进算法的有效性，它也也保证时间性能。

英文摘要：

Emerging technologies of wireless and mobile communication enable people to accumulate a large volume of time-stamped locations,which appear in the form of a continuous moving object trajectory.How to accurately predict the uncertain mobility of objects becomes an important and challenging problem.Existing algorithms for trajectory prediction in moving objects databases mainly focus on identifying frequent trajectory patterns,and do not take account of the effect of essential dynamic environmental factors.In this study,a general schema for predicting uncertain trajectories of moving objects with dynamic environment awareness is presented,and the key techniques in trajectory prediction arc addressed in detail.In order to accurately predict the trajectories,a trajectory prediction algorithm based on continuous time Bayesian networks（CTBNs） is improved and applied,which takes dynamic environmental factors into full consideration.Experiments conducted on synthetic trajectory data verify the effectiveness of the improved algorithm,which also guarantees the time performance as well.

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