Zhang Junjie, Cui Jianhua, Wang Zhongyong, Ding Yingqiang, Xia Yujie
School of Physics and Electronic Information, Luoyang Normal University, Luoyang 471934, China.
School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China.
Sensors (Basel). 2019 Sep 4;19(18):3829. doi: 10.3390/s19183829.
Location information is a key issue for applications of the Internet of Things. In this paper, we focus on mobile wireless networks with moving agents and targets. The positioning process is divided into two phases based on the factor graph, i.e., a prediction phase and a joint self-location and tracking phase. In the prediction phase, we develop an adaptive prediction model by exploiting the correlation of trajectories within a short period to formulate the prediction message. In the joint positioning phase, agents calculate the cooperative messages according to variational message passing and locate themselves. Simultaneously, the average consensus algorithm is employed to realize distributed target tracking. The simulation results show that the proposed prediction model is adaptive to the random movement of nodes. The performance of the proposed joint self-location and tracking algorithm is better than the separate cooperative self-localization and tracking algorithms.
位置信息是物联网应用中的一个关键问题。在本文中，我们关注具有移动智能体和目标的移动无线网络。基于因子图，定位过程分为两个阶段，即预测阶段和联合自定位与跟踪阶段。在预测阶段，我们通过利用短时间内轨迹的相关性来开发一种自适应预测模型，以制定预测消息。在联合定位阶段，智能体根据变分消息传递计算协作消息并进行自我定位。同时，采用平均一致性算法来实现分布式目标跟踪。仿真结果表明，所提出的预测模型能够适应节点的随机移动。所提出的联合自定位与跟踪算法的性能优于单独的协作自定位和跟踪算法。
