  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.
