高动态无人机编队网络自适应路由方法研究

With increasing development of future unmanned aerial vehicle (UAV) formation networks, networking of those networks with high time-varying and complex electromagnetic environment has been becoming a crucial challenge. Since that, this proposal is going to research and study the self-self-adaptive routing scheme using stability of the node and link reliability and link to locate and optimization using Bayesian network in order to improve the efficiency and satisfy both academic and practical demands in UAV formation networks. The future research includes: 1) for the high dynamic time-varying perception and evaluation problem, node stability evaluation based on multiple attribute decision, which is used to describe and forecast the dynamic changes of both links and available spectrum, and the corresponding the self-adaptive routing scheme; 2) for UAV formation network stability maintenance problems under complicated electromagnetic environment, given the applications’ demands and Bayesian network model, a fault link locating approach and nodes’ (UAVs’) optimal adjustment strategy as well as the corresponding reasoning algorithm based on influence diagram model is going to be designed in order to improve the stability and reliability of the network. Both will contribute to the design of the dynamic networking. Moreover, the key research points cover the node state-based stability evaluation method, self-adaptive routing mechanism based on the node stability and the reliability of the link, and fault link locating and self-adaptive optimization based upon Bayesian network.

本项目以未来无人机编队典型应用场景为背景，以提高未来无人机编队网络在高动态和复杂电磁环境下的自适应动态组网效能为切入点，探索基于节点稳定性和链路可靠性的自适应路由方法和基于贝叶斯网络的故障链路定位及优化方法，使之满足无人机编队网络动态组网在理论和应用中的需求。针对高动态无人机编队网络时变特性感知与评估关键科学问题，基于多属性判决的相关理论，研究刻画动态可用频谱和链路变化的节点稳定性判决模型以及以之为基础的自适应路由机制；针对复杂电磁环境下无人机编队网络稳定性维护关键科学问题，从先验的贝叶斯网络模型入手，找到故障链路位置；设计基于路障链路的影响图模型及其推理算法，得到节点最优调整策略，进一步改善组网的稳定性和可靠性，满足无人机编队网络高效动态组网需求。对基于节点状态的稳定性评估方法、基于节点稳定性和链路可靠性的自适应路由机制和基于贝叶斯网络的故障链路定位及自适应优化等关键问题展开研究。

随着自动化和传感器技术的进步，无人机在当前的诸多应用场景发挥了越来越多作用。由于单个无人机的能力有限，多个无人机以编队方式协同完成单个无人机无法完成的复杂使命。为了满足诸如协同合作等应用的需要，多跳无人机之间必须组网以实时交互必要的信息。无人机编队网络具有节点高速运动、节点与节点间通信质量不稳定、编队周边电磁环境复杂等特点，为无人机编队组网带来了巨大挑战。为了解决上述问题，本项目从提高无人机编队网络在高动态和复杂电磁环境下的自适应动态组网效能为切入点，对基于节点状态的稳定性评估方法、基于节点稳定性和链路可靠性的自适应路由机制和基于贝叶斯网络的故障链路定位及自适应优化等关键问题展开研究。根据研究计划，首先对多信道条件下的影响无线链路传输的MAC层和物理层进行分析和评估，得到了一手的实验数据，提出了一种适应于无人机编队网络的MAC协议；接着对节点稳定性、以及节点转发能力和链路稳定性评估与预测方法入手，提出了一种网络节点稳定性评估机制， 一种节点转发能力模型，一种链路容量模型；提出了基于节点稳定性的多径路由机制、基于节点转发能力和链路容量的数据转发机制，这些机制比传统的方法适应能力更强更有效；针对移动自组织网络的拓扑动态变化，提出了一种拓扑抗毁性评估方法和一种基于贝叶斯推理的链路劣化定位与路由重构方法； 首次提出了一种适用于无人机网络的网络编码的传输机制；发表高质量论文16篇（其中SCI 期刊论文6篇，CCF推荐会议论文10篇），发明专利6项，培养研究生7名，实现了项目的预期目标。