摘要：

传统的轨迹预测算法训练模型时需要耗费大量时间,且时空复杂度高、执行效率低,不能满足实时预测的需求。为此,提出一种改进的移动目标轨迹预测算法。基于欧氏距离进行轨迹相似度计算以提高预测准确性和实效性,根据最小描述长度原理对预测后的轨迹进行简化,优 化运算效率。实验结果表明,该算法能准确预测移动目标的轨迹,并且具有较低的算法复杂度,适用于海量数据背景下的移动目标轨迹预测。

关键词: 轨迹相似度, 轨迹预测, 移动目标, 最小描述长度, 遗传算法

Abstract:

Traditional trajectory prediction algorithms require a lot of time when training models,the complexity of time and space is too high,and the execution efficiency is too low to meet the needs of real-time prediction.Therefore,this paper proposes an advanced mobile target trajectory prediction algorithm.The algorithm calculates the trajectory similarity based on Euclidean distance to improve the accuracy and effectiveness of the prediction.The predicted trajectory is simplified based on the principle of minimum description length,which optimizes the calculation efficiency and trend display.Experimental results show that the algorithm can accurately predict the trajectory of the mobile target,and has a lower complexity of the algorithm,which is completely suitable for the trajectory prediction of the mobile target under the massive data background.

Key words: trajectory similarity, trajectory prediction, mobile target, minimum description length, genetic algorithm

中图分类号: 

• TP391