Optimal measurement policy for predicting UAV network topology
Optimal measurement policy for predicting UAV network topology
复制标题
预测无人机网络拓扑的最优测量策略
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Jacob Chakareski
中科院分区：
文献类型：
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作者：
Abolfazl Razi;F. Afghah;Jacob Chakareski
近年来，人们对使用无人驾驶飞行器（UAV）网络越来越感兴趣，这些网络共同执行各种应用的复杂任务。实现无人机网络的一个重要挑战是需要一个适应快速网络拓扑变化的通信平台。例如，及时预测网络拓扑变化，可以通过建立长时间连接的链路来降低通信链路的损失率。在这项工作中，我们为每架无人机开发了一种最佳跟踪策略，以感知其周围的网络配置，以促进更有效的通信协议。更具体地说，我们开发了一种基于粒子群优化和卡尔曼滤波的间歇观测算法，在时变信道质量和受限跟踪资源的情况下，为每架无人机找到一组最优跟踪策略，使整个网络估计误差最小。
In recent years, there has been a growing interest in using networks of Unmanned Aerial Vehicles (UAV) that collectively perform complex tasks for diverse applications. An important challenge in realizing UAV networks is the need for a communication platform that accommodates rapid network topology changes. For instance, a timely prediction of network topology changes can reduce communication link loss rate by setting up links with prolonged connectivity. In this work, we develop an optimal tracking policy for each UAV to perceive its surrounding network configuration in order to facilitate more efficient communication protocols. More specifically, we develop an algorithm based on particle swarm optimization and Kalman filtering with intermittent observations to find a set of optimal tracking policies for each UAV under time-varying channel qualities and constrained tracking resources such that the overall network estimation error is minimized.