基于声阵列的运动车辆航向估计算法

野外监控传感器网络通常会利用多个节点的信息来估计运动目标的航向。若单个传感器节点能够实现对运动目标航向的估计,将有效降低传感器网络的通信开销,并提高信息融合效率。在此背景下,文中提出一种适用于单体传感器节点的运动车辆目标航向角估计方法。该算法利用了节点内置的声阵列和电子罗盘数据,并针对野外环境中存在的风噪声和突发干扰进行了相应的处理策略设计。首先,对提出的航向估计算法理论进行了分析,将航向估计问题的主要研究内容细分为方位角估计问题和最邻近时刻估计问题;其次,对抗干扰方位角估计算法和最邻近时刻估计算法进行了介绍;最后为验证提出算法的效果,在采集的样本上进行了仿真实验,分别从风力大小和布设距离两个角度对实验结果进行分析,验证了所提算法对于野外环境下车辆目标航向角计算的有效性。

Energy-Delay Tradeoff for Online Offloading Based on Deep Reinforcement Learning in Wireless Powered Mobile-Edge Computing Networks

Benefited from wireless power transfer(WPT)and mobile-edge computing(MEC),wireless powered MEC systems have attracted widespread attention.Specifically,we design an online offloading scheme based on deep reinforcement learning that maximizes the computation rate and minimizes the energy consumption of all wireless devices(WDs).Extensive results validate that the proposed scheme can achieve better tradeoff between energy consumption and computation delay.