Distributed Spatio-Temporal Information Based Cooperative 3D Positioning in GNSS-Denied Environments

TL;DR

This paper introduces a distributed cooperative 3D positioning algorithm for GNSS-denied environments that improves accuracy and reduces computational complexity using a novel sampling method and anchor upgrading mechanism.

Contribution

The paper presents a new STICP algorithm that supports various ranging measurements, employs a scaled unscented transform for nonlinear message approximation, and includes an enhanced anchor upgrading mechanism.

Findings

Lower computational complexity than existing belief propagation methods

Achieves competitive 3D positioning accuracy in GNSS-denied environments

Supports diverse ranging measurement types

Abstract

A distributed spatio-temporal information based cooperative positioning (STICP) algorithm is proposed for wireless networks that require three-dimensional (3D) coordinates and operate in the global navigation satellite system (GNSS) denied environments. Our algorithm supports any type of ranging measurements that can determine the distance between nodes. We first utilize a finite symmetric sampling based scaled unscented transform (SUT) method for approximating the nonlinear terms of the messages passing on the associated factor graph (FG) with high precision, despite relying on a small number of samples. Then, we propose an enhanced anchor upgrading mechanism to avoid any redundant iterations. Our simulation results and analysis show that the proposed STICP has a lower computational complexity than the state-of-the-art belief propagation based localizer, despite achieving an even more competitive positioning performance.