基于TOF/TOA/TDOA的高精度移动无线传感器网络定位技术研究

本项目研究能够适应未来大规模低成本移动无线传感器网络的定位技术，重点研究TOF/TOA/TDOA高精度移动无线传感器定位技术，突破移动锚节点的定位和时钟同步、网络拼接和移动源节点定位等关键科学问题。本项目中所提出的算法不需要外部辅助定位和高精度时钟设备，也不需要复杂的协议。拟采用锚节点周期性广播机制，采用TOF双向测距、偏差TOF或TOA等三种算法实现锚节点的自主定位与同步，三种算法的复杂度逐次降低。网络拼接的核心是解决网络片段“翻转模糊”问题，通过小的网络片段来构建完整的网络拓扑。在源节点定位方面，拟采用基于TDOA钟差补偿的移动节点定位算法，锚节点的位置和同步信息用于对TDOA信息进行补偿。该算法的优点是移动源节点通过一次发射即可实现定位，因而具有最小的功耗。为克服定位同步最大似然估计非凸性的问题，本项目将采用基于半定规划等多种算法实现全局最优，保证估计算法的收敛。

TOF/TOA/TDOA based fine-grained localization technologies for mobile wireless sensor networks are studied in this project to meet the requirements such as large-scale, low-costs and rapid-deployment. The purpose of the project is to make breakthroughs on problems including localization and time synchronization of anchors, network stitching, and mobile source localization. It is no need for external positioning and timing devices, or complicated protocols. A periodical anchor broadcasting scheme is proposed to synchronize and localize anchors. Three algorithms are proposed to resolve the problems of localization and synchronization for mobile anchor nodes: (1) TOF based trilateral localization algorithm in which TOF ranging protocol is needed, (2) Biased-TOF based joint localization and synchronization algorithm where TOF ranging protocol is not needed, only TOAs and TOEs(Time of Emission) of anchor broadcasting scheme are enough for joint estimation of location and synchronization, (3) TOA based joint localization and synchronization algorithm in which only TOAs of broadcasting are needed. The key problem of network stitching is to deal with “flip ambiguity” and establish the whole topology of the network. By incorporating locations and timing information of anchors, original TDOAs is calibrated and used to localize mobile sources. The mobile source is located via a single transmission which means minimum energy dissipation. Owing to the nonconvex nature of the maximum likelihood estimation, we reformulate the joint localization and synchronization problem as weighted least squares problem and perform semidefinite relaxation to obtain a convex semidefinite programming problem and guarantee the convergence of the algorithm.