Cooperative Navigation Using Pairwise Communication with Ranging and Magnetic Anomaly Measurements

TL;DR

This paper presents a cooperative localization method for UAV groups in GNSS-denied environments, combining inter-vehicle ranging and magnetic anomaly measurements to estimate positions with about 20 meters error over 180 km.

Contribution

It introduces a two-step cooperative localization approach using EKF and particle filter, integrating ranging and magnetic anomaly data for improved UAV positioning.

Findings

Average position error of 20 meters after 180 km flight

Algorithms tolerate large variations in sensor noise

Adding UAVs improves localization robustness

Abstract

The problem of cooperative localization for a small group of Unmanned Aerial Vehicles (UAVs) in a GNSS denied environment is addressed in this paper. The presented approach contains two sequential steps: first, an algorithm called cooperative ranging localization, formulated as an Extended Kalman Filter (EKF), estimates each UAV's relative pose inside the group using inter-vehicle ranging measurements; second, an algorithm named cooperative magnetic localization, formulated as a particle filter, estimates each UAV's global pose through matching the group's magnetic anomaly measurements to a given magnetic anomaly map. In this study, each UAV is assumed to only perform a ranging measurement and data exchange with one other UAV at any point in time. A simulator is developed to evaluate the algorithms with magnetic anomaly maps acquired from airborne geophysical survey. The simulation results show that the average estimated position error of a group of 16 UAVs is approximately 20 meters after flying about 180 kilometers in 1 hour. Sensitivity analysis shows that the algorithms can tolerate large variations of velocity, yaw rate, and magnetic anomaly measurement noises. Additionally, the UAV group shows improved position estimation robustness with both high and low resolution maps as more UAVs are added into the group.