基于距离信息的无人机碰撞预测方案

UAV Collision Prediction Scheme based on Distance Information

卫星拒止环境下无人机安全问题是众多学者研究的热点。增加传感器数量虽然可以获得更多的信息,但是给无人机平台带来了更多的负担和干扰。因此,利用尽可能少的传感信息实现无人机群安全飞行值得研究。鉴于此,研究仅利用距离信息实现多个体之间的碰撞预测。预测方案中提出利用矩阵填充的方法弥补通信测距丢包的难题,使用均值检验进行不同运动模型识别,并借用克拉美-罗界分析预测方法的性能,进而给出了仿真分析。结果表明,提出的方案能有效区分不同运动模型,并在个体之间测距信息丢失后依然能有效实现碰撞预测。

The safety of UAVs in the satellite denial environment is a hot topic for many scholars.Increasing the number of sensors will obtain more information,but it also brings more burden and interference to the UAV platform.Therefore,using as little sensor information as possible to realize the safe flight of UAV swarms is a worthy research topic.In view of this,this paper only uses distance information to realize collision prediction between multiple objects.In the prediction scheme,a matrix filling method is proposed to make up for the problem of packet loss in communication distance measurement,and the mean value test is used to identify different motion models.It borrows the Cramer-Rao boundary analysis and prediction method,and makes a simulation analysis.The results indicate that the proposed scheme can effectively distinguish different motion models,and can still effectively realize collision prediction after the distance measurement information between individuals is lost.