针对无人机(UAV)与地面无人值守传感器(UGS)的空地协同目标追踪问题, 提出一种交通道路网络环境下基于局部搜索树的移动目标搜索追踪方法。在该方法中, 无人机通过与地面无人值守传感器抵近通信, 获取目标经过传感器节点的时间信息, 基于该信息估计目标运动速度及预测目标后续位置, 通过局部递归搜索优化无人机对目标的追踪路径。针对追踪过程中不完全信息条件下的传感器节点访问次序决策问题, 设计了两种节点选择评价机制并对其效果进行了比较和分析。仿真实验结果表明, 该方法能在目标运动路径及速度不断变化的情况下以较大概率捕获目标。

Aiming at air-ground collaboration of Unmanned Aerial Vehicle (UAV) with Unattended Ground Sensor (UGS) for target tracking, this paper proposes a searching and tracking method for moving targets based on local search trees in the traffic road network environment.In this method, the UAV is in close communication with an UGS to obtain the time information of the target passing through the node.Based on the information, the target motion speed is estimated and the subsequent target position is predicted.The path of UAV in target tracking is optimized through local recursive search.For the decision-making of sensor node accessing order with incomplete information in the tracking process, two kinds of node selection evaluation mechanisms are designed and analysis is made to their effects.Simulation results show that this method can capture targets with a higher probability when the targets moving path and speed change greatly.