3D Mobile Localization Using Distance-only Measurements
For multi-UAV systems operating in GPS-denied environments, this work provides a practical solution for relative localization using minimal sensing, though the approach is incremental.
This paper addresses 3D localization of cooperating UAVs using only noisy distance measurements, with only one UAV knowing its global coordinates. The proposed SDP-based algorithm with gradient descent refinement achieves localization with a minimum number of measurements, validated on experimental flight data.
For a group of cooperating UAVs, localizing each other is often a key task. This paper studies the localization problem for a group of UAVs flying in 3D space with very limited information, i.e., when noisy distance measurements are the only type of inter-agent sensing that is available, and when only one UAV knows a global coordinate basis, the others being GPS-denied. Initially for a two-agent problem, but easily generalized to some multi-agent problems, constraints are established on the minimum number of required distance measurements required to achieve the localization. The paper also proposes an algorithm based on semidefinite programming (SDP), followed by maximum likelihood estimation using a gradient descent initialized from the SDP calculation. The efficacy of the algorithm is verified with experimental noisy flight data.