Cooperative Positioning for Sparsely Distributed High-Mobility Wireless Networks with EKF Based Spatio-Temporal Data Fusion

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