This paper focuses on the problem of estimating bearing vectors between the agents in a two dimensional multi-agent network based on subtended angle measurements. We propose an edge localization graph to investigate the solvability of this problem and a distributed estimation method via orientation estimation of virtual agents to solve the problem. Under the proposed method, the estimated bearing vector exponentially converges to the real one with a common bias if and only if the edge localization graph has an oriented spanning tree. Furthermore, the estimated variables exponentially converge to the true values if the edge localization graph has an oriented spanning tree with a root knowing the bearing vector from it to one of its neighbors.

本文着重研究基于对角测量的二维多智能体网络中智能体之间的方位矢量估计问题。我们提出了一个边缘定位图来研究该问题的可解决性，并提出了一种通过虚拟代理的方向估计的分布式估计方法来解决该问题。在所提出的方法下，当且仅当边缘定位图具有定向的生成树时，估计的方位向量才以公共偏置指数收敛到实数。此外，如果边缘定位图具有定向生成树，并且根的根知道从其到其邻域之一的方位矢量，则估计变量将指数收敛至真值。