We propose a distributed cooperative positioning algorithm using the extended Kalman filter (EKF) based spatio-temporal data fusion (STDF) for a wireless network composed of sparsely distributed high-mobility nodes. Our algorithm first makes a coarse estimation of the position and mobility state of the nodes by using the prediction step of EKF. Then it utilizes the coarse estimate as the prior of STDF that relies on factor graph (FG), thus facilitates inferring a posteriori distributions of the agents' positions in a distributed manner. We approximate the nonlinear terms of the messages passed on the associated FG with high precision by exploiting the second-order Taylor polynomial and obtain closed-form representations of each message in the data fusion step, where temporal measurements by imperfect hardware are considered additionally. In the third stage, refinement of position estimate is performed by invoking the update step of EKF. Simulation results and analysis show that our EKF-STDF has a lower computational complexity than the state-of-the-art EKF-based algorithms, while achieving an even superior positioning performance in harsh environment.

翻译：针对由稀疏分布高动态节点组成的无线网络，提出一种基于扩展卡尔曼滤波（EKF）时空数据融合（STDF）的分布式协同定位算法。该算法首先利用EKF的预测步骤对节点位置与运动状态进行粗估计，继而将粗估计结果作为依赖于因子图（FG）的STDF的先验信息，从而以分布式方式推导各智能体位置的后验分布。通过利用二阶泰勒多项式对因子图上传递消息的非线性项进行高精度逼近，并在数据融合步骤中考虑非理想硬件产生的时域测量值，获得了每条消息的闭式表达。在第三阶段，通过调用EKF更新步骤实现位置估计的精化。仿真结果与分析表明，与当前最先进的基于EKF的算法相比，所提出的EKF-STDF方法在恶劣环境下不仅具有更低的计算复杂度，还能实现更优的定位性能。