基于大数据分析的移动对象轨迹预测方法

Moving object trajectory prediction method based on large data analysis

摘要:

对移动对象的轨迹预测将在移动目标跟踪识别中具有较好的应用价值。移动对象轨迹预测的基础是移动目标运动参量的采集和估计,移动目标的运动参量信息特征规模较大,传统的单分量时间序列分析方法难以实现准确的参量估计和轨迹预测。提出一种基于大数据多传感信息融合跟踪的移动对象轨迹预测算法。首先进行移动目标对象进行轨迹跟踪的控制对象描述和约束参量分析,对轨迹预测的大规模运动参量信息进行信息融合和自正整定性控制,通过大数据分析方法实现对移动对象运动参量的准确估计和检测,由此指导移动对象轨迹的准确预测,提高预测精度。仿真结果表明,采用该算法进行移动对象的运动参量估计和轨迹预测的精度较高,自适应性能较强,稳健性较好,相关的指标性能优于传统方法。

关键词:大数据分析;移动对象;轨迹预测;目标参量估计;信息融合

Abstract:

The trajectory prediction of moving objects has good application value in the tracking and recognition of moving objects. Moving object trajectory prediction is based on moving target motion parameters acquisition and estimation, moving target motion parameter information of the feature scale larger, traditional single component time series analysis methods difficult to achieve accurate parameter estimation and trajectory prediction. A moving object trajectory prediction algorithm based on multi sensor information fusion and tracking is proposed. Firstly moving object trajectory tracking control object description and constraint parameters analysis of trajectory prediction of mass movement parameter information of information fusion and self is a whole qualitative control, through data analysis method to move the object motion parameters accurate estimation and detection, thus to guide the accurate prediction of trajectories of moving objects and improve prediction accuracy. The simulation results show that the proposed algorithm is used to estimate the motion parameters of moving objects and the accuracy of trajectory prediction is higher, and the performance of the proposed method is stronger, and the robustness is better, and the related indexes are better than the traditional method.

Key words:large data analysis; moving object; trajectory prediction; target parameter estimation; information fusion