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

TL;DR

This paper introduces a distributed cooperative positioning algorithm for high-mobility wireless networks using EKF-based spatio-temporal data fusion, improving accuracy and reducing computational complexity in challenging environments.

Contribution

It develops a novel EKF-based spatio-temporal data fusion method utilizing factor graphs and second-order Taylor approximations for distributed high-precision positioning.

Findings

Lower computational complexity than existing EKF algorithms

Superior positioning accuracy in harsh environments

Effective distributed inference of node positions

Abstract

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.