Geo-Spatio-Temporal Information Based 3D Cooperative Positioning in LOS/NLOS Mixed Environments

TL;DR

This paper introduces a novel 3D cooperative positioning algorithm for wireless networks in mixed LOS/NLOS environments, leveraging geographic info, factor graphs, and advanced transforms for improved accuracy and efficiency.

Contribution

The paper presents a new GSTICP algorithm that integrates geographic info, NLOS identification, and a scaled unscented transform to enhance 3D positioning in complex environments.

Findings

Lower computational complexity than belief propagation methods

Achieves competitive positioning accuracy

Supports various ranging measurement types

Abstract

We propose a geographic and spatio-temporal information based distributed cooperative positioning (GSTICP) algorithm for wireless networks that require three-dimensional (3D) coordinates and operate in the line-of-sight (LOS) and nonline-of-sight (NLOS) mixed environments. First, a factor graph (FG) is created by factorizing the a posteriori distribution of the position-vector estimates and mapping the spatial-domain and temporal-domain operations of nodes onto the FG. Then, we exploit a geographic information based NLOS identification scheme to reduce the performance degradation caused by NLOS measurements. Furthermore, we utilize a finite symmetric sampling based scaled unscented transform (SUT) method to approximate the nonlinear terms of the messages passing on the FG with high precision, despite using only a small number of samples. Finally, we propose an enhanced anchor upgrading (EAU) mechanism to avoid redundant iterations. Our GSTICP algorithm supports any type of ranging measurement that can determine the distance between nodes. Simulation results and analysis demonstrate that our GSTICP has a lower computational complexity than the state-of-the-art belief propagation (BP) based localizers, while achieving an even more competitive positioning performance.