针对装甲车辆运动状态复杂性、战场态势不确定性、战术迷惑和欺骗性导致装甲车辆集群运动轨迹难以准确预测的问题,提出一种基于密度的空间聚类应用(Density-Based Spatial Cluste-ring of Applications with Noise,DBSCAN)和长短时记忆(Long Short Term Memory,LSTM)神经网络的装甲车辆集群轨迹预测方法.根据装甲车辆的斜坡上行驶、转向和车-车交互行驶状态,建立运动学模型.选取机动特征、环境特征和车-车交互特征等轨迹特征信息,基于双层LSTM网络预测单个装甲车辆的轨迹.基于DBSCAN算法将多条单装预测轨迹进行分段、相似度计算和聚类,获得集群代表轨迹作为装甲车辆集群的预测轨迹.仿真结果表明,所提方法能够有效预测装甲车辆集群轨迹,实现料敌于先、谋敌于前.
Armored Vehicle Cluster Trajectory Prediction Method Based on DBSCAN Clustering Algorithm and LSTM Network
It is difficult to accurately predict the movement trajectory of armored vehicle cluster due to the complexity of armored vehicle motion states,the uncertainty of battlefield situations,and the tactical confusion and deception.This paper proposes a trajectory prediction method for armored vehicle cluster based on density-based spatial clustering of applications with noise(DBSCAN)clustering algorithm and long short-term memory(LSTM)network.Firstly,a kinematic model of armored vehicles is established based on the states of armored vehicles driving on slopes,turning and interacting with other vehicles.And then the trajectory characteristics such as maneuver features,environmental features and vehicle-to-vehicle interaction features are selected,and the trajectory of an individual armored vehicle is predicted using a dual-layer LSTM network.Finally,the DBSCAN algorithm is utilized to segment the multiple single-vehicle predicted trajectories,calculate the similarities among them,and cluster them to obtain a representative trajectory for the cluster,as the predicted trajectory for the armored vehicle cluster.Simulated results demonstrate that the proposed method can effectively predict the trajectories of armored vehicle clusters.
armored vehiclecluster trajectory predictiondensity-based spatial clustering of applications with noiselong short-term memory networktrajectory prediction system
万方数据
维普
